BIM Best Practices for Construction Health and Safety: A Systematic Review

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BIM Best Practices for Construction Health and Safety: A Systematic Review
Adeeb Sidani a; João Poças Martins b ; Alfredo Soeiro c;
a Faculty of Engineering, University of Porto, Porto, Portugal (adeeb.sidani@hotmail.com); b CONSTRUCT, Faculty of Engineering, University of Porto,
BUILT COLAB, Porto, Portugal (jppm@fe.up.pt); c Faculty of Engineering, University of Porto, Porto, Portugal (avsoeiro@fe.up.pt);
Abstract
Construction health and safety is a critical aspect of the construction industry, with potential risks to workers and
project success. Building Information Modelling (BIM) and digital tools have emerged as promising technologies
to enhance safety practices in construction projects. This systematic review aims to identify and analyse the best
practices of BIM implementation for construction health and safety.
Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement, a
comprehensive literature search was conducted in major scientific databases. The inclusion criteria focused on
scientific articles related to BIM and digital tools for construction health and safety. A total of 106 relevant
articles were selected for detailed analysis.
The systematic review identified several key best practices for BIM implementation in construction health and
safety. These practices encompassed various stages of the construction project lifecycle, including design, pre-
construction planning, construction, and maintenance activities. The findings highlight the importance of
integrating BIM with other digital tools, such as virtual reality, augmented reality, and sensor technologies, to
enhance safety practices.
The review also emphasised the significance of collaboration among project stakeholders, including architects,
engineers, contractors, and safety professionals, throughout the implementation of BIM for construction health
and safety. Furthermore, the integration of BIM with safety regulations and standards was identified as a crucial
aspect to ensure compliance and mitigate risks.
This systematic review provides a framework linking digital technologies with the theories of accident
causations. The findings offer valuable insights for construction industry professionals, researchers, and
policymakers interested in leveraging BIM and digital tools to improve safety practices. Future research
directions are suggested to address the challenges and limitations identified in the existing literature.
Keywords
Construction; Building Information Modelling (BIM); Occupational Safety; Occupational Health.
Introduction
1.1 Construction Sites Conditions
Construction sites are unique and require specific planning and management due to the dynamic and complex
nature of construction projects (Melzner et al., 2013). The space on construction sites is often limited, and various
activities, workers, materials, and equipment need to be accommodated. The layout and organization of the
workspace constantly change throughout different stages of the construction process. This can lead to conflicts
and disorganization if not properly managed (Getuli et al., 2020). In construction, there is a specific need to
manage the priorities and needs of the stakeholders ensuring a smooth project, and manage and coordinate the
activities of different work crews and trades to ensure that workers, resources, and equipment are constantly
available (Winge et al., 2019).
Construction sites are inherently hazardous, with a wide range of activities taking place simultaneously. The
construction activities are long-lasting and dynamic with a strict never halting schedule, overlapping tasks and
objectives, including various equipment and heavy machinery. The construction process is recognized for having
a fixed product while the construction process is ongoing, resulting in conflicts in site organization and workspace
disorganization (Zhang & Hu, 2011). Furthermore, there is a wide range of activities that operate simultaneously
on the construction site, including carpentry, roofing, welding, excavation, demolitions, among others. Workers
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often operate in risky conditions such as working at heights, in narrow spaces, with electricity, harmful
substances, or around heavy machinery. Failure to pay attention to safety can compromise workers' safety and
health. (Zhang, Teizer, Pradhananga, & Eastman, 2015).
The statistics highlight the significance of safety in the construction industry. In 2019, the Occupational Safety
and Health Administration (OSHA) recorded around 199,000 injuries and illnesses in the AECO (Architecture,
Engineering, Construction, and Operations) sector in the United States. Construction accounted for one in five
worker deaths in the same year (U.S. Department of Labor; Bureau of Labor Statistics, 2019). The European
Union also experienced a significant number of fatal accidents in the construction sector, with one-fifth of all
fatal accidents at work occurring within this industry in the EU-28 in 2017 (Eurostat, 2017a).
Portugal ranked 4th in Europe with an accident rate of 2.94 fatalities per 100,000 workers in the same year
(Eurostat, 2017b). In 2018 and 2019, Portugal saw a decrease in fatalities and serious occupational accidents, but
the numbers still indicate the need for effective safety management in the construction industry (ACT, 2019)
(Rodrigues et al., 2021).
In particular, 58.6 percent of fatalities among construction workers in 2020 were due to the fatal four, consisting
of falls, struck-by-objects, electrocution, and stuck-in (Labor, 2019). It is evident that construction sites present
inherent risks and challenges that need to be addressed through effective safety management and prevention
strategies.
1.2 Accident Causation and Prevention Theories
Accident causation theories provide valuable insights into understanding the causes of accidents in the
construction environment and offer strategies for accident prevention. Several prominent theories have been
developed over the years.
Heinrich's Accident Triangle and Bird's Modified Accident Pyramid are two influential models in accident
causation theory. Heinrich proposed the Accident Triangle, which suggests that addressing smaller accidents and
unsafe acts can reduce the occurrence of major accidents and fatalities (Heinrich, 1931). Bird further developed
this concept with the Modified Accident Pyramid, which highlights the progressive nature of accidents and
emphasizes the importance of addressing near misses and unsafe conditions (Abdelhamid & Everett, 2000;
Bellamy, 2015; Bird, 1990; Heinrich, 1931).
To prevent accidents, Heinrich also introduced the Domino Theory, which posits that accidents can be prevented
by eliminating the factors preceding them, represented as dominos (Heinrich, 1959). Bird expanded on this theory
by highlighting the crucial role of management in accident prevention, emphasizing factors such as motivation
to work safely and hazard recognition (Abdelhamid & Everett, 2000; Bird, 1990).
Petersen's Multiple Causation Model suggests that accidents are typically caused by multiple factors, and
understanding these underlying causes is essential in preventing unsafe acts and conditions (Petersen, 1971).
Building on these theories, Wang proposed a comprehensive Theory of Accidents Causes and Prevention,
identifying five significant factors to eliminate in order to prevent accidents: environment and heredity,
management, personal factors, job factors, and unsafe actions and conditions (Y. Wang, 2018). Wang further
developed the Zero Incident Safety Management (ZISM) model, which combines these causal factors with six
safety management factors, aiming to achieve zero incidents and eliminate hazards through proactive safety
planning and implementation (Figure 1) (Y. Wang, 2018). In addition to these theories, it is important to consider
the role of safety culture in accident prevention. A strong safety culture, characterized by shared values, beliefs,
attitudes, and behaviors related to safety, can foster proactive accident prevention strategies and promote a safe
work environment.
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Figure 1 Wang's Theory of Accidents Causes and Prevention (Wang, 2018)
The Construction Industry Institute (CII) developed the Nine Zero Injury Principles, which have been widely
accepted in the construction industry to attain zero injuries. These principles focus on management commitment,
worker participation, safety planning, training, and other factors (CII1994).
James Reason's Swiss Cheese Model highlights the importance of layered defences to prevent accidents. It
suggests that multiple layers of defence can mitigate risks.
It is important to acknowledge the limitations of relying solely on opinion-based risk data as a safety information
source. Such data are subjective and prone to biases, making it crucial to explore alternative sources of
information and incorporate objective empirical data for a more comprehensive understanding of construction
safety risks (Tixier et al., 2017; Hale et al., 2012; Papazoglou & Ale, 2007).
By considering these accident causation and prevention theories, along with the limitations of safety information
sources, it will be possible to develop effective strategies and practices to improve construction site safety and
reduce accidents.
1.3 Emerging Technologies and Safety Integration in Construction: Building Information Modelling and
Beyond".
In response to the limitations of traditional safety strategies in the construction industry, researchers and
professionals have turned their attention to emerging technologies and intelligent systems that are commonly
used for design, planning, or operations. Examples of such technologies include Building Information Modeling
(BIM), proximity sensing, and information retrieval. However, these efforts currently face limitations as the data
used are often secondary, aggregated, and subjective, and tasks are considered in isolation, which hinders the
effective capture of the dynamic nature of construction work (Tixier et al., 2017).
Although BIM has proven to be a valuable tool for supporting construction management in the early planning
phase (Eastman et al., 2008; Zhang and Hu, 2011), its application to safety is still in its early stages. Zhang et al.
(2011, 2013) introduced a framework for safety in BIM, proposing a safety rule-checking system that
automatically applies fall protection measures to a building information model (Melzner et al., 2013).
In recent years, automation and digitalization have offered the construction sector opportunities for higher
performance, accuracy, cost reduction, and modernization. BIM exemplifies this paradigm shift in the industry,
providing advantages in collaboration, scheduling, 3D drawings, management, quantity estimation, and material
sorting. Several countries have already embraced BIM integration, supported by European Directive 2014/EU,
particularly Article 22 which promotes the use of BIM in public procurement, and the international standard for
BIM (ISO 19650) (Zhang & Hu, 2011).
However, the adoption rate of BIM has been slower than anticipated (Zhang & Hu, 2011). Industry key players
need to understand how to effectively implement and utilize BIM, exploring opportunities for accessing,
managing, and exchanging BIM data. This calls for more integrative and flexible approaches that can leverage
the diverse profiles and academic backgrounds of construction project stakeholders, workers, daily tasks, and
tacit knowledge.
1.4 Research Objectives
The application of BIM-based health and safety in the AECO sector holds significant potential for improving
project coordination, risk reduction, and worker safety. In this work, our objective is to conduct a systematic
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review that investigates the various aspects of BIM-based health and safety applications. The review aims to
answer critical questions and provide a comprehensive appraisal of the most recent approaches, reasoning, major
risks targeted, standards and regulations, hardware and software, target groups, and assessment methods used in
the development of BIM-based health and safety applications.
The following research questions will be considered and addressed in this systematic review:
Q1. What are the safety approaches that are implemented in construction projects?
Q2. What is the Objective of BIM-based health and Safety implementation in construction projects?
Q3. At which stages of the Project lifecycle are the Health and Safety techniques implemented?
Q4. What are the limitations of traditional health and safety techniques, and to what extent are recent technologies
improving these approaches?
Q5. What are the major risks being targeted, and what methodologies are authors adopting to prevent them? Are
these methodologies being implemented onsite or offsite?
Q6. Which standards and regulations are being followed in the implementation of BIM-based safety measures?
Q7. What are the current limitations of BIM-based safety, and are they being addressed in recent research?
Q8. Who are the main target groups benefiting from BIM-based health and safety applications?
Q9. How are BIM dimensions and the concept of BIM Levels of Development (LODs) and Levels of Information
Need (LOIN) influencing BIM requirements and their application in health and safety practices?
By addressing these research questions, this systematic review aims to contribute to the advancement of BIM-
based health and safety practices in the construction industry. The findings will provide valuable insights for
improving project outcomes, enhancing worker safety, and fostering collaboration among various stakeholders.
2 Methodology
The methodology of the current systematic review is the published and registered PRISMA-P (Sidani et al.,
2022). PRISMA-P, launched in 2009, is known for its reproducibility and its ability to accelerate the development
of high-quality syntheses in the field of science and technology research. By adopting this methodology, the
authors aimed to ensure the review's quality, enable readers to assess its strengths and weaknesses, facilitate
replication of the review methods, and structure the review using the PRISMA checklist (Page et al., 2021).
All the necessary research steps were described, including the identification of information sources, the search
strategy employed, the criteria for study inclusion and exclusion, and the tools used to assess bias within the
eligible studies. The main modification to the protocol pertains to the timeframe. After discovering that the first
article related to BIM-based health and safety was published in 2009 (Eastman et al., 2009), the authors decided
to include studies from 2009 to 2022 to ensure the inclusion of recent and relevant literature.
Section 3 of this article begins by classifying the included articles, presenting their main characteristics,
illustrating their objectives, and highlighting the essential information necessary to address the previously
formulated research questions. Section 4 assesses the content of the articles, provides answers to the research
questions proposed earlier, describes identified research trends, and discusses the limitations of the research.
Section 5 proposes a Framework for the implementation of best practices in the AECO Sector based on the
analysis of the identified limitations. Additionally, it presents a Theoretical Causation and Prevention Model that
integrates new technologies. Finally, Section 6 concludes the review and discusses future considerations.
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3 Results
3.1 Selected articles
In the research phase of the systematic review, a total of 195 articles were initially compiled. The exclusion
criteria were then applied, resulting in the removal of 49 articles based on article type, 17 articles based on subject
area, and 2 articles due to language restrictions. After removing duplicates using Mendeley, 19 additional articles
were excluded. Subsequently, 32 articles were excluded based on the analysis of titles and abstracts, as they were
deemed off-topic. This led to a total of 119 articles being excluded.
Following the approach proposed by Wohlen (2014), a snowballing methodology was applied to the references
of the selected articles, resulting in the addition of 45 more articles. The remaining 121 articles underwent further
assessment for eligibility. Out of these, 15 articles were rejected, including four conference papers and 11 articles
that were out of scope or did not align with the objectives of the review regarding the use of BIM for health and
safety (Wohlin, 2014).
Figure 2 illustrates the research process using a PRISMA 2020 flow diagram, adapted from PRISMA-P (Page et
al., 2021), It shows a final count of 106 papers that met the inclusion criteria.
Figure 2 PRISMA 2020 flow diagram for a systematic review.
3.2 Presence of bias
Bias in research studies refers to any tendency or intervention that can modify or alter the results of a given
problem (Gerhard, 2008). Understanding bias is crucial for conducting high-quality research. Various factors
contribute to bias, including interference with the inclusion criteria of the population, exposure factors of test
subjects, and assessment and analysis interference (Gerhard, 2008). Additionally, systematic errors in sampling,
examination, or evaluation that indirectly or directly support a particular outcome are considered bias ( (Pannucci
& Wilkins, 2010).
In the selected articles of this systematic review, the presence of bias may be argued, as some studies had a small
sample size of less than 5 individuals, which were used for case studies followed by questionnaires. Due to the
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significant differences in the characteristics and experiences of such a small sample, it is anticipated that there
may be a minor negative effect on the conclusions drawn. Furthermore, some samples were selected from the
same facility with a similar background for questionnaires, which might introduce some misleading aspects to
the assessment methods.
It is important to note that the existence of "selection bias" does not invalidate the entire article or its conclusions.
Rather, it emphasizes the need for critical analysis of the article's framework and results. For example, while
usability analysis may be affected, the proposed interventions, functionality methods, and architectural
frameworks, which are the main focus of this article, are not entirely compromised. Therefore, no articles were
omitted from the review as none of the identified biases significantly impacted the answers to the proposed
research questions.
3.3 Characteristics of the included studies
In this section, we provide a summary of the 106 articles included in our review, highlighting the year with the
most publications (Figure 3) and the journals with frequent publications (Figure 4). The publication period of the
article’s ranges from 2008 to 2022. In 2022, the highest number of articles 21 was published, followed by 2021
17, then 2015 and 2019 with 13 and 15 articles respectively. The year 2018 also had a significant number of
publications with 12 articles. It is important to note that since our search was conducted at the end of 2022, the
number of articles published in 2022 is expected to increase by the end of the year. This trend indicates a growing
interest in the field of BIM-based health and safety in recent years.
The selected articles were published in 36 different journals. The journal with the most publications is
Automation in Construction, with 31 articles. The second most prolific journal is the Journal of Safety Science.
Other journals that have made contributions to this field include Advances in Civil Engineering and Construction
Engineering and Management, each with five articles. Additionally, Construction Innovation Engineering,
Construction and Architectural Management, Advanced Engineering Informatics, and Information Technology
in Construction have three publications each. Finally, 28 different journals had only one or two publications
related to BIM-based health and safety.
Figure 3 Number of Published Articles per year
1
3
1
2
4
14
7
4
12
14
6
17
21
0
5
10
15
20
25
2008
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
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Figure 4 Frequent Journal Publications
3.4 Construction Stages, Fields, Target Groups
The safety fields addressed by the authors are shown in (Figure 5). Safety management is the field with the most
mentions with 32 mentions. Visualisation and Monitoring have the almost same number of mentions with 25 and
26 respectively. Thirdly, Automated Rule Checking has 21 mentions. Safety Planning and Risk Assessment were
addressed 17 times each. The lowest number of mentions were that of Safety Training and Education with seven
and Inspection with only four mentions. A brief explanation of each safety field is provided below:
Safety management: Refers to the strategies and processes implemented to manage safety within construction
projects.
Visualisation: Involves the use of visual representation techniques and tools to enhance safety understanding
and communication.
Monitoring: Involves the continuous tracking and surveillance of construction activities to identify potential
safety risks.
Automated Rule Checking: Refers to the use of automated systems and algorithms to check compliance with
safety rules and regulations.
Safety planning: Involves the development of safety plans and protocols to ensure a safe working
environment.
Risk Assessment: Involves the identification and assessment of potential risks and hazards within
construction projects.
Safety training and education: Focuses on providing training and education programs to enhance safety
awareness and knowledge.
Inspection: Involves the inspection and examination of construction sites and processes to ensure compliance
with safety standards.
31
8
5
5
3
3
3
3
0
5
10
15
20
25
30
Automation in Construction
Safety Science
Advances in Civil Engineering
Construction Engineering and Management
Construction Innovation
Engineering, Construction and Architectural Management
Advanced Engineering Informatics
Information Technology in Construction
Number of Publications
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Figure 5 Safety Fields Targeted by Authors
The objectives of the articles and the developed solutions mainly target the construction phase of the project life
cycle, with 63 authors focusing on this phase (Figure 6). The second and third most addressed phases are design
and pre-construction, with 33 and 27 mentions, respectively. Facility management had the least mentions, with
only two articles (Malekitabar et al., 2016; Riaz et al., 2017). A brief explanation of each construction stage is
provided below:
Construction phase: Refers to the actual construction activities and processes involved in the project.
Design phase: Involves the conceptualization and development of the project design, including architectural,
structural, and MEP (mechanical, electrical, plumbing) aspects.
Pre-construction phase: Encompasses the activities that occur before the actual construction begins, such as
site preparation, permitting, and procurement.
Facility management: Pertains to the management and maintenance of the constructed facility after its
completion.
Figure 6 Project Lifecycle Targeted by the Authors
It is worth noting that various articles may use different terms for the same target groups. To simplify the
categorization, the authors considered general terms. For example, "Designers" represents both architects and
designers, and "Construction Managers" combines site managers and construction managers. Safety
professionals and safety managers were considered as "Safety Managers." Additionally, any specialized engineer
was categorized as "Engineers" due to the lack of specificity in the articles. Articles that mentioned multiple
target groups are counted more than once.
The articles primarily focus on Safety Managers with 77 mentions (Figure 7). Designers and Construction
Managers are the second and third most mentioned target groups with 24 and 20 mentions, respectively. Workers
and Engineers have a lower number of mentions with 14 and 10, respectively. Facility Managers were identified
as the primary target group in two articles (Getuli et al., 2020; Pham et al., 2020). Similarly, students were
primary target groups in two articles (Bhagwat et al., 2021; Clevenger et al., 2015). While owners were
mentioned twice as secondary target groups (Arslan et al., 2019c; Wu et al., 2015).
34
26
25
21
17
17
7
4
Safety Management
Monitoring
Visualization
Automated Rule Checking
Safety Planning
Risk Assessment
Safety Training and
Education
Inspection
63
33
27
2
0
20
40
60
Construction
Design
Pre-construction
Facility Management
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Figure 7 Target Groups of the Collected Articles
3.5 Targeted Risks
In this subsection, we explore the risk categories addressed by the collected articles. (Figure 8) provides an
overview of the targeted risks mentioned. The collected articles cover 19 different risk categories, some of which
are addressed simultaneously by multiple authors.
Roofers and carpenters are addressed once by Lu (Lu et al., 2021). Similarly , risks associated with masonry
works are mentioned once by Zhang (S. Zhang, Boukamp, et al., 2015). Likewise, hand-arm vibration syndrome
(HAVS) is targeted by BuHamdan (BuHamdan et al., 2021). Also, electrocution and noise risks are addressed
by Farghaly (Farghaly et al., 2022).
Transportation and heavy machinery related risks are targeted three times by Arslan and Getuli, (Arslan et al.,
2019b; Getuli, Capone, & Bruttini, 2021; Hasan et al., 2022).
Atmospheric Hazards such as toxic gases, high or low temperatures and humidity, and dust particles were
mentioned by four authors (Arslan et al., 2014; Cheung et al., 2018; P. Liu, 2022; Smaoui et al., 2018).
Fire related hazards were mentioned five times (Hosseini & Maghrebi, 2021; Marzouk & Daour, 2018; Mirahadi
et al., 2019; S. Park & Kim, 2015; Y. Yang et al., 2022).
Risks related to crane usage is mentioned five times (Al Hattab et al., 2018; Alizadehsalehi et al., 2020; Clevenger
et al., 2015; Golovina et al., 2016; J. P. Zhang & Hu, 2011).
Likewise, confined spaces / stuck in between had five mentions (Arslan et al., 2019b; Golovina et al., 2019; Ji &
Leite, 2018; H. Li, Lu, Chan, et al., 2015; Marzouk & Daour, 2018).
Hazardous behaviour and hazardous environmental risks are two broad categories involving various risks.
Hazardous behaviours risks are mainly due to the unsafe actions or equipment misuse, while hazardous
areas\environment involves risks situated in specific areas, or due to natural causes such as rainfalls, (Figure 8).
Hazardous behaviours are the focus of eight articles, accounting for approximately 7% of the total articles
(Bhagwat et al., 2021; Costin et al., 2015; Dong et al., 2018; Getuli, Capone, & Bruttini, 2021; Lee et al., 2019;
H. Li, Lu, Hsu, et al., 2015; Ragnoli et al., 2022).
On the other hand, hazardous environmental risks are mentioned in six studies (Arslan et al., 2014, 2019b;
Cheung et al., 2018; P. Liu, 2021; Tran et al., 2021; M. Zhou et al., 2021).
Construction risks related to scaffolding are addressed in six studies (Bhagwat et al., 2021; Clevenger et al.,
2015; Hara et al., 2019; K. Kim et al., 2016, 2018b, 2018a).
Struck by falling objects or moving machinery is mentioned seven times, with authors developing safety planning
approaches and visualizing models to organize the construction site and overcome any overlapping activities.
Struck by incidents are considered as secondary risks always addressed alongside near misses or falls (Abed et
77
24
20
14
10
2
2
2
0
10
20
30
40
50
60
70
80
safety Managers
Designers
Construction Managers
Workers
Engineers
Students
Facility Managers
Owners
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al., 2019; Ahn et al., 2020; Arslan et al., 2019b; Choe & Leite, 2017; Golovina et al., 2016; Pham et al., 2020; S.
Zhang, Teizer, et al., 2015).
Near-miss hazards are targeted as primary risks and are mentioned seven times (Arslan et al., 2019a, 2019c;
Golovina et al., 2016, 2019; H. Li, Lu, Chan, et al., 2015; J. Park et al., 2017; X. Shen & Marks, 2016).
Likewise, collision is mentioned seven times, with authors primarily using visualization systems to prevent
collisions (Al Hattab et al., 2018; Arslan et al., 2019b; Hu & Zhang, 2011; Moon et al., 2014; Yi et al., 2015; C.
Zhou et al., 2019).
Excavation and underground related risks are targeted by 13 authors. Excavation is mentioned eight times, while
underground risks are mentioned five times (Akula et al., 2013; M. S. Khan et al., 2021; N. Khan et al., 2019;
M. Li et al., 2018; X. Li et al., 2022; Liang & Liu, 2022; S. Park & Kim, 2015; Tian et al., 2021; Wu et al., 2015;
Xie et al., 2022; Xun et al., 2022; L. Zhang et al., 2016; Y. Zhang et al., 2021).
General worksite risks are targeted in 15 articles. This category includes common risks such as general site
inspection for construction hazards, general bridge-related construction risks, and prefabrication and mounting-
related risks. The authors excluded general tunnel and underground risks from this category as they were
considered part of the excavation risks. Additionally, general risks associated with scaffolding, cranes, and falls
were considered as specific risks.
The general risks were addressed using various approaches. The most notable approach was safety management,
mentioned 11 times, to prevent general construction risks. Risk assessment was used as a secondary approach by
eight authors. Several authors developed automated risk identification systems (Alizadehsalehi et al., 2020; Bao
et al., 2022; Collado-Mariscal et al., 2022; H. Kim et al., 2015, 2016; Mihić et al., 2018; Ramos-Hurtado et al.,
2022; Sampaio et al., 2022; Y. Shen et al., 2022; Tixier et al., 2017; Zou et al., 2016).
Falling from heights is the most frequently mentioned risk category, with 25 mentions. Authors primarily focused
on addressing falling risks 16 times and as secondary risks six times(Abed et al., 2019; Afzal & Shafiq, 2021;
Ahn et al., 2020; Arslan et al., 2019b; Choe & Leite, 2017; Collinge et al., 2022; Farghaly et al., 2022; Herzanita
et al., 2022; M. M. Hossain & Ahmed, 2019; Lee et al., 2019; B. Li et al., 2022; P. Li et al., 2022; D. Liu et al.,
2020; Malekitabar et al., 2016; J. Park et al., 2017; S. Park & Kim, 2015; Pham et al., 2020; Rodrigues et al.,
2021; Q. Shen et al., 2022; J. Wang et al., 2015; B. Yang et al., 2022; S. Zhang et al., 2013; S. Zhang, Sulankivi,
et al., 2015; S. Zhang, Teizer, et al., 2015).
Figure 8 Targeted Risks
1
1
1
1
2
3
4
5
5
5
6
6
7
7
7
9
13
24
25
0
5
10
15
20
25
30
Roofers/Carpenters
Masonry Works
Hand Arm Vibration Syndrome (HAVS)
Noise
Electrocution
Trasportation and Heavy Machinery
Atmospheric Hazards (Gases, Temperature, Humidity, Dust)
Fire
Cranes
Confined Spaces / Stuck in Between
Hazardious Environments Risks (Rain fall)
Scaffolding
Struck by
Near-miss
Collisions
Hazardious Behaviour
Excavation and Undeground (3)
General Worksite Risks
Falls
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3.6 BIM Dimensions Analysis
Dimensions for BIM are used to describe the information and the approach of a BIM model (Kamardeen, 2010),
where each dimension represents a pre-established set of information. Yet, there is a lack of general agreement
and multiple definitions for each dimension, mostly beyond the 5D (Williams et al., 2015). This systematic
review is in line with the investigations performed by Charef and Kamardeen. Charef conducted a systematic
review to determine the number of BIM dimensions there are after the 5th dimension (Charef et al., 2018).
Whereas Kamardeen propose a schematic concept for 8D BIM for Prevention through Design (PtD) to
incorporate safety knowledge (Kamardeen, 2010). Thus, this review considers BIM dimensions as:
3D – Geometry;
4D – Scheduling;
5D – Costs;
6D – Sustainability;
7D – Facility Management;
8D – Safety.
The most utilized BIM dimension for health and safety is 4D with 56 mentions, (Figure 9). Every article assessed
the BIM’s model geometry and some of the elements’ properties, and the authors considered articles focusing on
geometry, rendering and other element’s properties as a 3D model which was exploited 43 times. Although, three
of the six articles mentioned that they are using 4D BIM but added cost, which the authors considered it as a 5D
model (K. Kim et al., 2018a; Marzouk & Daour, 2018; Tak et al., 2021). In total, 5D was used six times while
only two authors confirmed using 5D (Cortés-Pérez et al., 2020; Lu et al., 2021). Moreover, one article considered
a 8D which is safety (Sampaio et al., 2022). Similarly, one author mentioned 3D BIM model and added cost,
which the authors also considered as a 5D mode (J. Wang et al., 2015).
This concludes that for most health and safety solutions, 4D models are the ideal solution whereas 3D models
are sufficient for general visualization, collision detection, and model walkthroughs. The results demonstrate the
limited diversity in exploring BIM dimensions within the health and safety field, which indicates missed
opportunities for research areas to leverage BIM's full potential. In addition, there is a misconception and
confusion concerning BIM's dimensions, since studies concerning PtD should have referred to 8D BIM model
concepts and if using cost, the model should directly be described as 5D.
Figure 9 Identified BIM Dimensions
3.7 Implementation location
The authors developed various types of tools to enhance construction safety, considering different
implementation locations: onsite, offsite, or a combination of both Figure 10. A majority of the proposed systems,
totaling 62 articles, were designed for offsite use. These offsite tools offer the advantage of centralized operations
and can be utilized in controlled environments, such as manufacturing facilities or prefabrication yards. They
43
56
6
0
10
20
30
40
50
60
3D
4D
5D
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enable safety measures to be implemented during the fabrication and assembly stages before transporting
components to the construction site.
Additionally, 22 systems were specifically designed for a mixed-use approach, which involves using the tools
both onsite and offsite. This hybrid implementation allows for seamless coordination between offsite and onsite
activities, optimizing safety measures throughout the construction process. The mixed-use tools can facilitate
collaboration, information exchange, and real-time monitoring between different stakeholders, including offsite
fabrication teams and onsite construction crews.
Furthermore, 24 systems were exclusively developed for onsite use. These tools are specifically tailored to
address safety challenges and risks that are inherent to the construction site environment. Onsite tools often focus
on real-time hazard identification, worker tracking, and immediate communication of safety alerts. They provide
valuable support for construction activities, enabling timely interventions and reducing the likelihood of
accidents or incidents.
It is important to note that while the implementation locations varied, some tools and systems could be adapted
for multiple locations based on project requirements and specific safety needs.
Figure 10 Location of the Intervention
3.8 Standards and Regulations
The authors mentioned Occupational Safety and Health Administration (OSHA) 20 times. Some authors
incorporated OSHA regulations along with local or national regulations, while others adapted their standardized
database based on OSHA standards. However, 48 authors developed safety tools without considering any specific
standards or regulations. Several articles referred to national standards such as the Building Regulation in the
UK (BuHamdan et al., 2021; Cortés-Pérez et al., 2020; Farghaly et al., 2022; M. A. Hossain et al., 2018). The
American safety regulation level the Japanese safety regulation level (Cheung et al., 2018). Standard for safety
inspection of building construction” in China (Yuan et al., 2019). Abu Dhabi Occupational Safety and Health
Center (OSHAD) requirements (Afzal & Shafiq, 2021). Bangladesh National Building Code (BNBC-2006) (M.
M. Hossain & Ahmed, 2019). Bureau of Indian Standards (BIS) codes (regulatory) (Bansal, 2011; Bhagwat et
al., 2021). Italian National laws of Health and Safety in Workplaces (Getuli, Capone, Bruttini, et al., 2021).
Portuguese safety legal regulations (Rodrigues et al., 2021). And the Iraqi safety Regulations (Abed et al., 2019).
Additionally, ten authors collected standards and guidelines through surveys, academic publications, and a
combination of regulations from various construction companies (M. M. Hossain & Ahmed, 2019; H. Kim et al.,
2016; Lee et al., 2019; B. Li et al., 2022; M. Li et al., 2018; Mihić et al., 2018; Tixier et al., 2017; Yuan et al.,
2019; J. Zhang et al., 2022; Zou et al., 2016).
In the context of BIM and occupational risk prevention, it is worth mentioning that some countries have
developed specific safety standards through BIM methodology. Examples include the "Common BIM
Requirements" in Finland (COBIM, 2012), "PAS 1192–6 – Specification for Collaborative Sharing and Use of
Off-site
56
On-site
16
Off-site/On-site
19
Off-site
On-site
Off-site/On-site
Location
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Structured Health and Safety Information Using BIM" in the UK (BSI, PAS 1192–6, 2018), "Building
Information Modelling Site Safety Submission Guidelines and Standard" by the New York Department of
Buildings (New York County Buildings, 2013), and "The BIM Guide" by the Building Construction Authority
in Singapore (BCA, 2013).
Two important BIM standards, namely the Level of Development (LOD) and the Level of Information Need
(LOIN), are particularly relevant in the context of occupational risk prevention. LOD is specifically designed for
model-based work, while LOIN aims to describe the project information and specific needs at a given moment
(Daniotti et al., 2020). The literature indicates that LOD has been more commonly used than LOIN in practice.
However, with the extended definition of LOIN in the EN standards and ISO 19650 standards, its application to
projects has increased.
Recently, the BIM Technical Committee CEN/TC 442 of the CEN-CENELEC Management Center developed
the EN 17412-1:2020 European standard for LOIN and deliveries of a BIM model. This standard outlines the
concepts and principles for defining the level of information need and deliveries during a project's lifecycle,
focusing on information exchange processes. It aims to deliver the right level of information at a specific stage
of the project, providing significant benefits to stakeholders. The standard also emphasizes the importance of
specific information delivery and the avoidance of unnecessary information to simplify the validation process.
Furthermore, the document defines the information to be exchanged according to the Exchange Information
Requirements (EIR). These standards were developed to address conflicts arising from various EIR schemes and
practices at the international and European levels. The document targets all stakeholders involved in the building
lifecycle, including owners, clients, operators, project managers, designers, contractors, manufacturers, technical
specialists, investors, regulatory authorities, insurers, and end-users. The EN 17412-1:2020 standard is based on
two main standards, namely EN ISO 19650-1, which focuses on the organization and digitization of information
using BIM for buildings and civil engineering, and EN ISO 29481-1, which is related to BIM and information
delivery manuals.
3.9 Methodology and employed hardware and software “simulation models and file exchange”
In order to reduce accidents and injuries in the construction site, the authors proposed to use several digital tools
alongside BIM. The authors used Autodesk Revit as the main BIM software 42 times. Tekla Structures was used
three times (S. Zhang et al., 2013; S. Zhang, Sulankivi, et al., 2015; S. Zhang, Teizer, et al., 2015). Similarly,
AutoCAD was also used three times(Hu & Zhang, 2011; M. S. Khan et al., 2021; Tak et al., 2021). Whereas,
Bentley AECOsim Building Designer was used only once (Wu et al., 2015).
Several other programs were used to develop animations, simulations, or to assist the integration of VR, AR,
sensors, and laser scanners. The most notable programs used are Navisworks used 12 times (Ahn et al., 2020;
Cheung et al., 2018; Deng et al., 2019; Ding et al., 2016; Hosseini & Maghrebi, 2021; S. Park & Kim, 2015;
Pham et al., 2020; Tak et al., 2021; Tian et al., 2021; Wetzel & Thabet, 2015; Yi et al., 2015; M. Zhou et al.,
2021). 3D Unity, which is a Game Engine was used seven times (Bhagwat et al., 2021; Dong et al., 2018; Getuli
et al., 2020; Getuli, Capone, & Bruttini, 2021; Getuli, Capone, Bruttini, et al., 2021; H. Li, Lu, Chan, et al., 2015;
H. Li, Lu, Hsu, et al., 2015), whereas Unreal Game Engine was used just once. Solibri model checker was used
three times (M. M. Hossain & Ahmed, 2019; Ji & Leite, 2018; S. Park & Kim, 2015). GIS programs were used
four times for tracking the location of the workers (Bansal, 2011; M. S. Khan et al., 2021; J. Wang et al., 2015;
M. Zhou et al., 2021).
Moreover, the nine authors implemented systems based on sensors, all sensors used are wireless with Bluetooth
or Wi-Fi. seven out of nine authors utilized sensors for real-time environmental monitoring such as wind speed,
temperature, humidity, air quality (Arslan et al., 2014; Cheung et al., 2018; Z. Liu et al., 2021; Riaz et al., 2014,
2017; Smaoui et al., 2018; C. Zhou et al., 2019).
VR was implemented seven times by the authors(Afzal & Shafiq, 2021; Bhagwat et al., 2021; Getuli et al., 2020;
Getuli, Capone, & Bruttini, 2021; Getuli, Capone, Bruttini, et al., 2021; Moon et al., 2014; Pham et al., 2020).
AR was mentioned three times (Akula et al., 2013; C. S. Park & Kim, 2013; Pham et al., 2020)
Similarly, laser scanners were also used three times (Akula et al., 2013; J. Wang et al., 2015; C. Zhou et al.,
2019)
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In addition, several file formats were utilized to ensure better collaboration and compatibility among different
programs. The authors utilized IFC 26 times. Other formats, such as FBX, OBJ, and dgn, were used just once.
3.10 Evaluation and assessment “survey trends”
Most authors conducted case studies or proof of concept experiments to validate the effectiveness of their
proposed solutions. 63 authors, engaged in case studies, site trials, and training experiments to assess the practical
application of their methods. 17 of the 63 authors conducted a survey, questionnaire, or an interview for the
professionals or trainees participating in the case study. The case studies were mainly implemented on the
construction site or on an ongoing construction project. 11 Authors just made a simple proof of concept, such as
doing an in-situ laboratory tests (Arslan et al., 2014, 2019b, 2019c; Getuli, Capone, Bruttini, et al., 2021;
Golovina et al., 2019; M. S. Khan et al., 2021; P. Li et al., 2022; Riaz et al., 2014; Smaoui et al., 2018; Tixier et
al., 2017; Yi et al., 2015).
4 Discussion
4.1 Summary of Evidence: Addressing Research Questions
A1. The safety approaches that are implemented in construction projects can be categorized based on their
frequency of occurrence. The most commonly implemented safety approaches, listed in order of frequency,
are:
Safety management
Visualization
Monitoring
Automated rule checking
Safety planning
Risk assessment
Safety training and education
Inspection
These approaches play a crucial role in enhancing safety practices and mitigating risks in construction projects
(Figure 5).".
A2. The general objective of BIM-based health and safety implementation in construction projects is to assist
stakeholders in various aspects of the project. The main objectives include:
Improving decision-making and collaboration between project teams.
Training workers and newcomers for specific tasks.
Facilitating site inspection and monitoring.
Identifying health and safety risks at earlier stages of the project.
Assisting in health and safety planning and management.
Applying suitable preventative measures.
Supporting facility management and evacuation planning.
These objectives highlight the broader benefits of integrating BIM-based health and safety practices into
construction projects, aiming to enhance project outcomes and ensure the well-being of workers.
A3. The implementation of health and safety techniques in construction projects occurs at various stages of the
project lifecycle. The most focused stage for implementing these techniques is the construction phase, where
safety measures are actively applied to ensure the well-being of workers and mitigate risks. Additionally,
health and safety techniques are also incorporated during the design phase to address potential hazards and
safety considerations. In the pre-construction phase, preliminary safety planning and risk assessments take
place to establish a safe working environment before the actual construction begins. Lastly, health and safety
measures continue to be relevant during the facility management phase to ensure ongoing safety and
maintenance of the constructed facility.
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A4. Construction projects are complex and dynamic in nature, posing challenges for safety planning. Traditional
health and safety measures rely heavily on manual inspection, monitoring, and management. However, they
are time-consuming, inaccurate, and struggle to keep up with changing construction schedules. The
uniqueness of construction activities exposes workers to various health and safety risks. Zhang (2015)
outlines the health and safety process, which involves utilizing the construction schedule to identify tasks,
highlighting risks, designing preventative strategies, and integrating them into the schedule.
The traditional health and safety process consists of two phases: preconstruction and construction. During
preconstruction, safety issues are identified based on prior experience, and safety planning and training
sessions are organized. However, these trainings cannot be generalized due to the dynamic nature of
construction operations. In the construction phase, health and safety hazards are mitigated through site
inspection and monitoring (Afzal & Shafiq, 2021).
Health and safety measures during the construction project lifecycle begin with preparation, where 2D
drawings, schedules, regulations, and past experiences are discussed to identify future hazards. However,
this approach is inefficient and error-prone due to the lack of visualization (Afzal & Shafiq, 2021). Safety
planning traditionally relies on method statement documents, which lack a structured approach and fail to
encompass most hazardous scenarios. Site inspection and monitoring are done manually using checklists,
which can be challenging for safety professionals to detect all hazards (J. Park et al., 2017).
Traditional worker training methods, such as lectures and PowerPoint presentations, are not effective for
construction workers. They prefer interactive learning approaches, and language and communication barriers
further complicate safety training in multilingual environments (Gao et al., 2019). Safety planning involves
various stakeholders and phases, making it challenging to maintain clear instructions throughout the process
(Afzal & Shafiq, 2021).
Recent technologies have the potential to improve health and safety approaches in construction projects.
BIM, digital twins, virtual reality, and augmented reality offer interactive visual models to assess
construction timelines and identify hazards that are not possible with traditional 2D drawings (Azhar, 2017).
Automated rule checking using BIM models can enhance safety planning and improve workers' safety
perception (Zhang et al., 2013). Technologies like RFID, drones, and UAVs facilitate real-time site
monitoring and inspection (Costin et al., 2015) (Alizadehsalehi et al., 2020). VR and AR technologies
provide immersive and interactive training environments, overcoming language barriers and engaging
construction workers effectively (Afzal & Shafiq, 2021) (Zhang & Teizer, 2017).
In conclusion, traditional health and safety planning in construction projects face challenges due to their
complexity, time-consuming nature, and manual processes. Language barriers and cultural differences
further complicate safety planning and training (Shafiq et al., 2020). However, recent technological
advancements offer promising solutions to enhance health and safety planning, monitoring, inspection, and
training. Implementing technologies such as BIM, digital twins, VR, AR, drones, and RFID can improve
safety practices, mitigate risks, and protect workers on construction sites.
A5. The authors developed various approaches to target in total 19 different risks. he authors developed various
approaches to target a total of 19 different risks. The risks targeted include roofers, carpenters, masonry
works, Hand-Arm Vibration Syndrome (HAVS), electrocution, noise, transportation-related risks, heavy
machinery, atmospheric hazards, fire-related hazards, crane usage, confined spaces/stuck in between safety,
BIM-based safety training, hazardous behaviors, hazardous environments, scaffolding, struck by falling
objects or moving machinery, near-miss hazards, collision-related risks, excavation and underground-related
risks, and general worksite risks.
Roofers and carpenters are addressed by Lu, who proposed an offsite quantitative risk assessment model
considering three indexes: likelihood, consequence, and exposure. This model uses BIM to calculate the risks
and assist designers and architects in choosing alternative solutions (Lu et al., 2021).
Zang addressed the risks associated with masonry works by exploring an offsite automated hazard and safety
task scheduling approach. This approach organizes, stores, and reuses construction safety knowledge,
connecting it with BIM to improve safety in masonry works (S. Zhang, Boukamp, et al., 2015).
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BuHamdan proposed an onsite discrete event simulation (DES) framework related to Cross Laminated
Timber (CLT) connectors. This framework provides visualization and assessment of the potential risk of
exposure to Hand-Arm Vibration Syndrome (HAVS) (BuHamdan et al., 2021).
Farghaly addressed electrocution and noise risks using an approach based on ontology and BIM. The offsite
decision-making tool combines physical models with health and safety concepts, such as location and
construction and operation activities (Farghaly et al., 2022).
Regarding transportation-related risks, Arslan proposed an offsite and onsite system to monitor workers'
movements on the construction site by collecting their trajectory data and enriching it with related semantic
information to prevent transportation-related risks (Arslan et al., 2019b). In addition, heavy machinery is
addressed offsite by Getuli with VR simulations (Getuli, Capone, & Bruttini, 2021).
Studying atmospheric hazards, Arslan created an onsite/offsite real-time environmental monitoring,
visualization, and notification system for health and safety management (Arslan et al., 2014). Cheung created
a real-time construction safety monitoring system for hazardous gases, integrating wireless sensor networks
and BIM (Cheung et al., 2018). Meanwhile, Smaoui developed an onsite/offsite respirable dust monitoring
and visualization in BIM using real-time sensor data (Smaoui et al., 2018).
Fire-related hazards were addressed by Park, who focused on resolving building safety issues through an
onsite/offsite BIM-based quality check process (S. Park & Kim, 2015). Mirahadi developed EvacuSafe as an
offsite designer's tool to allow for layout optimization of buildings in terms of their evacuation safety
performance (Mirahadi et al., 2019). Fire in the construction site and evacuation were addressed by two
authors. Hosseini developed a model to analyze the risk of fire emergency occurrence and evacuation
performance risks through an integrated approach in complex construction sites (Hosseini & Maghrebi,
2021). And Marzouk presented a framework that assists contractors and safety managers in planning labor
evacuation for construction sites using BIM and computer simulation by modeling the appropriate
construction method alternatives (Marzouk & Daour, 2018).
In addition, risks related to crane usage were addressed by four authors proposing an onsite real-time
monitoring approach, while Zhang suggests an offsite framework that integrates automated rule checking for
4D BIM models to review tower crane plans (Al Hattab et al., 2018; Alizadehsalehi et al., 2020; Clevenger
et al., 2015; Golovina et al., 2016; J. P. Zhang & Hu, 2011).
Similarly, confined spaces/stuck in between safety monitoring and visualization is proposed by five authors
(Arslan et al., 2019b; Golovina et al., 2019; Ji & Leite, 2018; H. Li, Lu, Chan, et al., 2015; Marzouk & Daour,
2018).
BIM-based safety training approaches using VR technologies are proposed by six authors (W. Lee et al.,
2015; R. Wang et al., 2018; Y. Wang et al., 2018; Xu et al., 2014; Y. Zhang et al., 2020; S. Zhu et al., 2020).
Concerning hazardous behaviours, Dong introduces a novel approach towards automated remote assessing
and monitoring the use of Personal Protective Equipment (PPEs) by integrating pressure sensors and tracking
devices and assess the workers performance based on their response to danger warnings (Dong et al., 2018).
Lee proposed a dynamic analysis and visual tracking approach based on behaviour-based safety system
(BBS), the study includes a checklist and unsafe worker’s behavior records (Lee et al., 2019). Li, proposed
an extension to the BBS approach, adding a proactive based safety (PBBS), to improve the earlier system.
The system has several functions, automatically monitoring location-based behaviours, quantitatively
measuring safety performance, investigating potential causes of unsafe behaviours, and improving the
efficiency of safety management (H. Li, Lu, Hsu, et al., 2015). Costin applied FRID-BIM to generate real-
time data to produce leading indicators for building protocol control. The integration of the technology
produces a real-time compliance, location tracking and zone safety violation maintaining building protocol
control (Costin et al., 2015). Bhagwat and Getuli developed a VR based systems, Bhagwat implemented
three visualization modules to improve the traditional visualization methods (Bhagwat et al., 2021). While
Getuli developed a standardized protocol for a viable integration of BIM and VR for training of workers in
real project environment (Getuli, Capone, & Bruttini, 2021).
On the other hand, targeting Hazardous Environments Zhou proposed a BIM-based system for automatically
identifying environmental risks and improve the efficiency of the special design and promote the digital
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transmission of risk design information throughout construction (M. Zhou et al., 2021). Liu developed system
that can capture various parameter about the environment and surrounding conditions and estimate the
rainfall for a particular region (P. Liu, 2021). Likewise, Tran proposed a spatial–temporal exposure system
called HISTEA, the system integrate scenarios of accident into E91:Q914D BIM. This approach assists the
construction team in detecting and preventing hazards especially repetitive accidents caused by overlapping
activities (Tran et al., 2021). An automatic identification and classification system was created by Arslan,
the system identifies the environmental risks and improve the efficiency of the spatial design (Arslan et al.,
2019b). Assisting with Realtime visualization, Arslan developed plugins for risk prevention, the system is
created to manage environmental safety management making Realtime monitoring, visualization and
notification possible (Arslan et al., 2014). Using Realtime monitoring as well, Cheung proposed a system
that enables the construction site to visually monitor the safety status via a spatial, coloured interface and
remove any hazardous gas automatically (Cheung et al., 2018).
Construction Risks related to scaffolding were addressed by Kim which integrated temporary structures into
automated safety checking using BIM for safety hazard identification and prevention (K. Kim et al., 2016).
Moreover, Kim in a more recent work introduces a decision making framework for scaffolding, which creates
safe scaffolding plans without excessive manual effort and sacrifice of other important construction goals
(K. Kim et al., 2018a). Furthermore, Kim explored a BIM-based decision support system for temporary
structure planning. Focusing on automatically generating multiple scaffolding plans by identifying relevant
potential safety hazards to enhance decision making (K. Kim et al., 2018b). Hara proposed a semi-automated
design procedure methods for temporary scaffolding (Hara et al., 2019). On the other hand, Clevenger
focused on safety training, the goal was to test the interactive BIM training modules to enhance
communication to improve the student’s learning (Clevenger et al., 2015). Likewise Bhagwat proposed
safety training using VR technology comparing several visualization platforms to figure out the preferred
and most effective training methods (Bhagwat et al., 2021).
For struck by falling objects or moving machinery mainly authors developed a safety planning approach and
visualizing models to organize the construction site and overcome any overlapping activities. Struck by was
considered as a secondary risk that is being targeted always alongside near misses or falls. Arslan developed
a semantic enrichment of spatiotemporal trajectories of workers this will monitor workers movements on the
construction site by collecting data and enriching it with semantic information (Arslan et al., 2019b).
Likewise, to improve safety planning, Choe developed a 4D construction safety planning framework that
addresses site specific temporal and spatial safety information integration taking into consideration risky
activities, which days and in what areas, then prioritize them according to the project schedules (Choe &
Leite, 2017). Another safety planning approach was mentioned by Golovina, which presented a method for
recoding, identifying, and assessing situations including heavy machinery. Golovina presented a heat map
generation for prediction of hazards and assist in safety planning by using a spatiotemporal GPS data
(Golovina et al., 2016). Also, for safety planning, Zhang developed a workforce location tracking model that
is able to visualize and analyse workspace requirements in BIM, thus enabling activity level site planning
(S. Zhang, Teizer, et al., 2015). Moreover, Pham created a 4D BIM based automatic site planning which
allocates temporal safety facilities (TSFs) for designated model objects/ conditions integrating activity based
risk assessment and related prevention approaches (Pham et al., 2020).
On the other hand, Abed created a visualization of the BIM model to accurately determine some of the
construction site hazards. The developed model assisted the safety managers in understanding the details and
sequence of work easily (Abed et al., 2019). A different approach to prevent struck by falling objects of
heavy machinery was proposed by Ahn. Ahn suggests to improve the effectiveness of safety training at
construction worksite using 3D BIM simulations (Ahn et al., 2020).
Also, Near-miss hazards were targeted as a primary risk. Authors focused on visualization and site
monitoring to prevent Near-miss risks. Four authors proposed real-time site monitoring to prevent near-
misses. Park Proposed an automated safety monitoring using cloud-enabled BIM and BLE mobile tracking
sensors, the system will capture and register potential hazards then a real-time tracking of construction
resources will be processed to communicate the information over the cloud for the stakeholders (J. Park et
al., 2017). Also using real-time monitoring, Arslan proposed a semantic trajectory for worker’s safety in
dynamic environments. The system is proposed to understand the worker’s and objects movements named
‘WoTAS’ (Worker Trajectory Analysis System) (Arslan et al., 2019a). Golovina proposed a similar approach
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using Realtime monitoring, a heat map generation for predictive safety planning was proposed, to capture,
process and analyse the workers data, the system is a GPS based system that automatically analyse a hazard
index (Golovina et al., 2016). In a recent work for Golovina an algorithm for quantitative analysis of close
call events and personalized feedback was proposed, this allows a simplified geometric information retrieval
in real-time (Golovina et al., 2019).
The three remaining authors focused on visualization to prevent near-misses. Arslan proposed a visualizing
intrusion framework for dynamic building environments using BLE, the data of the worker will be pre-
processed and mixed with spatiotemporal workers trajectory to be able to visualize the BIM model and
identify intrusions (Arslan et al., 2019c). similarly, Shen proposed a Near-miss information visualising tool
using BIM (Shen & Marks, 2016). finally, Li proposed a proactive training system for safe precast
installation, the prototype is called the Proactive Construction Management System (PCMS), it enables
training for precast installation and get familiar with the associated risks (H. Li, Lu, Chan, et al., 2015).
Considering collision related risks, the author’s main approach to prevent collisions was with visualization
systems. Hu solution was based on 4D and BIM visualization for conflicts and structural safety problems
during construction (Hu & Zhang, 2011). Moon created a BIM based algorithm for a schedule that minimizes
the simultaneous interference level on the worksite (Moon et al., 2014). Hattab investigated collision in
cranes by sequencing the critical and noncritical activities and the planning of crane operations (Al Hattab et
al., 2018). On the other hand, two authors focused on real time monitoring to solve collision issues. Arslan
developed the Semantic enrichment of spatiotemporal trajectories for tracking worker´s movements and
enriching it with the relevant semantic information (Arslan et al., 2019b). while Zhou created a cyber physical
monitoring system (CPS-SMS) for blind hoisting and underground constructions (C. Zhou et al., 2019).
Using safety management approach Yi developed a 4D BIM-based visualization system to simulate the
construction activities and collision detection (Yi et al., 2015).
Concerning excavation and underground related risks the authors adopted various strategies to prevent risks.
Four authors developed safety monitoring systems, Tian created an intelligent early warning system for
tunnels, “ANN-BIM” (Tian et al., 2021). Akula developed a Realtime drill monitoring and control system
using BIM and AR to identify hazardous scenarios and improve discission making (Akula et al., 2013).
Similarly Lian developed a warning system with real-time dynamic monitoring based on internet of things
and BIM model (Liang & Liu, 2022).Wu suggested a monitoring system to identify and understand the blind
spots when doing an urban underground assessment with mitigation methods to reduce risks (Wu et al.,
2015).Three authors developed automated rule checking systems, Khan developed an automated system
based on an algorithm using visual programming language that generates geometric conditions in BIM and
Visualize the potential risks and safety resources installation (N. Khan et al., 2019). Zhang developed a
semantic IFC data model for automatic risk identification (Y. Zhang et al., 2021). Li, created an automated
safety risk recognition mechanism for underground construction based on BIM (M. Li et al., 2018). Two
authors proposed safety management systems to overcome excavation risks, Park developed a BIM-based
quality control checking process (S. Park & Kim, 2015). Similarly Park proposed a BIM-based Risk
Identification Expert System (B-RIES), this system is used to overcome low efficiency in the traditional
information extraction (L. Zhang et al., 2016). Finally, to prevent excavation risks Khan proposed a
Geotechnical property modelling and safety zoning based on BIM and GIS systems, this system provides
information about the soil type, properties, depth, and volume to plan for safe construction activities (M. S.
Khan et al., 2021).
For the category of general worksite risks, the authors targeted these risks with different approaches the most
noticeable approach is offsite safety management which was mentioned 11 times to prevent general
construction risks. And risk assessment was used mainly as a secondary approach by eight authors. Several
authors created an offsite automated risk identification systems, such as Kim developed an automated
framework for information retrieval of past accident cases to assist safety managers prepare preventative
measures and increase the workers attention for construction risks (H. Kim et al., 2015). Similarly, Mihic
developed a construction hazard database for automated hazard identification through BIM (Mihić et al.,
2018). Tixier developed a clash detection system to identify incompatibilities among fundamental attributes
using data mining (Tixier et al., 2017). While Alizadehsalehi developed an onsite risk identification system
using unmanned aerial vehicles (UAV) and BIM to identify all types of hazards in the design phase and to
monitor safety performance in the construction phase (Alizadehsalehi et al., 2020). Kim also proposed
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another automated hazard area identification by developing a tailored risk breakdown structure (RBS) linking
it with BIM (H. Kim et al., 2016). Likewise, using RBS Zou developed a system for bridge construction
safety (Zou et al., 2016).
Authors used several methods for safety training and visualization, such as AR, gamification, tracking
devices (C. S. Park & Kim, 2013). Similarly Getuli used VR site scenarios and produced various smart
objects library items to reduce the time for developing the VR scenarios (Getuli, Capone, Bruttini, et al.,
2021). Bangal used a visualization training approach for engineering students and construction professionals
with a VR game (Bhagwat et al., 2021). Bansal used a 4D GIS system to help with integrating geospatial
editing with spatial and nonspatial information in a single environment to improve collective decision making
(Bansal, 2011).
Designing for safety, Hossain presented an offsite design for safety rule-based knowledge library to provide
safety knowledge for designers (M. A. Hossain et al., 2018). Likewise, Yuan integrated prevention through
design and BIM to automatically assess safety risks during the design phase, and improve the designers
understanding of constructions risks (Yuan et al., 2019).
To prevent risks related to falling offsite Automated rule checking was the approach that was adopted by
most authors with eight mentions. Hossain developed an automated safety checking system to identify fall
related hazards in the preconstruction phase (M. M. Hossain & Ahmed, 2019). Liu developed an automated
warning system based on IPS-IMU and BIM 3D models to provide safety notifications based on safety
regulations (D. Liu et al., 2020). Rodrigues developed safety plugins for risk prevention through design
allowing additional functionality to BIM (Rodrigues et al., 2021). Yang made an automatic detection of
falling hazards from surveillance videos based on BIM and computer vision (Yang et al., 2022). Li created
a logic based domain model of construction safety called SafeConDM to assist with construction safety
planning and decision making (B. Li et al., 2022). Zhang developed a BIM-based fall identification and
prevention system for safety planning (S. Zhang, Sulankivi, et al., 2015). Moreover, Zhang developed an
algorithm for the design phase which will make an automatic safety checking of construction models and
schedules (S. Zhang et al., 2013). Finally, Melekitabar developed a system to identify more than 40% of
potential fatalities in construction projects (Malekitabar et al., 2016).
Safety planning and a primary research field having visualization or risk assessment as a secondary approach
to tackle falling risks offsite were proposed by the authors. Choe defined a 4D construction safety planning
process that addresses site-specific temporal and spatial safety information integration (Choe & Leite, 2017).
Likewise, Pham considered a 4D model for site planning allocating temporary safety facilities for designated
model objects (Pham et al., 2020). Zhang provided an onsite worker location tracking to model system for
visualizing and safety planning (S. Zhang, Teizer, et al., 2015). Lee developed a Dynamic Analysis and
Visual Tracking of Key Factors based on Behaviour-based Safety and BIM (Lee et al., 2019). Li suggested
a BIM based system integrating Voxels to detect falling from heights hazards (P. Li et al., 2022).
Safety management was mentioned three times as an approach to overcome fall related risks. Farghaly
formed the foundation of an offsite decision-making system, based on ontology and BIM, integrating health
and safety concepts along with the BIM models, location, and construction and operation activities (Farghaly
et al., 2022). Abed by relying on the knowledge of the safety experts developed an offsite BIM approach to
accurately determine fall hazards and assist with decision making (Abed et al., 2019). Park proposed to
resolve the safety issues by an offsite BIM-based quality checking process (S. Park & Kim, 2015).
Onsite Real-time monitoring was adopted three times by the authors. Arslan, proposed a worker movement
monitoring system by collecting raw spatiotemporal trajectory data and enriching it with relevant semantic
information (Arslan et al., 2019b). Wang proposed to improve safety culture and behaviour using range point
cloud data and rule checking in BIM (J. Wang et al., 2015). similarly, Park proposed an automated
construction safety monitoring system using cloud-enabled BIM and BLE mobile tracking sensors (J. Park
et al., 2017).
Offsite Safety training was implemented two times to reduce falling hazards. Both authors used VR and 4D
models for training and collaboration (Afzal & Shafiq, 2021; Ahn et al., 2020).
The approaches developed were mainly mostly used offsite, some approaches were found to be used onsite
or mixed use between onsite and offsite. The location of the use depends on the type of type of risks, target
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groups and construction fields the approaches are targeting. For example, most of the training approaches
were made to be offsite to train workers before entering the workspace, automated rule checking was also
targeting the design and preconstruction stages to check the models and develop safety planning before the
project starts. Likewise, visualization tools are mainly used offsite for planning and facility management.
While monitoring tools are both onsite and offsite use since they track workers or construction equipment
and at the same time safety professionals use them offsite to analyse the data. On the other hand, tools for
inspection are used onsite.
These developments demonstrate the potential of implementing safety-enhancing tools across different
locations within the construction lifecycle. However, it is crucial to acknowledge the challenges and
limitations associated with each implementation location. Factors such as logistical constraints,
communication issues, and adaptability to diverse work environments should be carefully considered during
tool deployment.
Additionally, exploring the integration and synergies between onsite and offsite tools can lead to more
comprehensive safety management practices. By leveraging the strengths of both approaches, construction
projects can achieve enhanced efficiency, improved coordination, and a higher level of overall safety
performance.
A6. In each country, health and safety regulations and standards for construction activities are established
differently. The authors have previously mentioned the standards and regulations used, and in this section,
they will list some of the main laws in several countries and identify at which stage the regulations are applied
and the responsibilities of the stakeholders.
Numerous standards and regulations have been established and implemented worldwide to avoid
construction accidents and promote worker safety. It is important to note that while the following examples
provide an overview of regulations in selected countries, there are many more regulations that exist globally.
In the United States, the Occupational Safety and Health Act (OSH Act) was formed in 1970, placing the
responsibility for construction safety on employers. Under the OSH Act, employers are obliged to provide a
safe and healthy working environment for their employees, which is recognized as the General Duty Clause
(U.S. Department of Labor, 2019). Consequently, preventative measures and safety planning have improved
construction safety management. The safety management is thought to be a systematic and broad practice for
managing safety hazards. Safety management can be divided into four main components as per the British
Standards Institution: first, safety policy and safety commitment of top management. Second, safety risk
management, such as risk detection, hazard assessment, and risk control. Third, safety implementation and
training. Finally, safety monitoring and inspection (Choe & Leite, 2017). The OSH Act has set minimum
guidelines to protect the health and safety of construction workers, and compliance with these guidelines is
critical for construction companies operating in the United States.
In Canada, the Canadian Occupational Health and Safety Regulations (SOR/86-304) outline the requirements
and responsibilities for employers, employees, and other stakeholders in ensuring workplace health and
safety in the construction industry.
In Europe, the general standard followed by most countries is the European Directive 92/57/CEE. This
directive provides a framework for ensuring the health and safety of workers in the construction industry.
For example, the Portuguese Law DL 273/2003 is based on the European directive and mandates that during
the conceptual and design phase, risk prevention measures must be associated, and a clear health and safety
plan should be developed(Rodrigues et al., 2021). Similarly, in the United Kingdom, the Construction Design
and Management Regulations 2015 (CDM 2015) outline the requirements and obligations of stakeholders
throughout the construction project lifecycle to ensure the health and safety of workers (Perry, 2016).
France also has its own set of regulations known as the Coordination and Coordination and Construction
Safety and Health Plan (Plan de Coordination et de Coordination et de Sécurité et de Protection de la Santé,
or PCCS-PS), which requires the coordination of safety and health measures during the construction phase
(Ministère de la Transition Écologique et Solidaire, 2016). Germany's regulations are also aligned with the
European directive and are considered internationally significant due to the dual protection system. The
Baustellenverordnung BaustellV standards for construction safety in Germany are similar to the OSHA
regulations, placing the responsibility on the contractor to provide adequate health and safety environments
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for construction workers. German regulations also require health and safety specialists to participate in the
design and construction planning process and communicate the necessary health and safety measures to be
implemented at each stage. During the construction stage, safety professionals should continuously update
the health and safety plan in response to schedule alterations or subcontractors' activities (Melzner et al.,
2013).
Moreover, it is worth noting that in addition to the general health and safety regulations, there are specific
standards and guidelines related to Building Information Modeling (BIM) and occupational risk prevention.
These standards aim to enhance safety practices within the construction industry by leveraging BIM
technology. Examples of such standards include:
The "Common BIM Requirements" in Finland (COBIM, 2012) provide guidelines and requirements for
integrating safety considerations into BIM processes and workflows.
The "PAS 1192–6 – Specification for collaborative sharing and use of structured health and safety
information using BIM" in the UK (BSI, PAS 1192–6, 2018) establishes standards for the collaborative
sharing and utilization of health and safety information within BIM models.
The "Building information Modelling Site Safety Submission Guidelines and Standard" by the New York
Department of Buildings (New York County Buildings, 2013) provides guidelines and requirements for
incorporating safety information into BIM models specifically for construction sites.
The "The BIM Guide" by the Building Construction Authority in Singapore (BCA, 2013) offers guidance
on implementing BIM technology for improved safety practices in construction projects.
International standards such as ISO 45001: Occupational Health and Safety Management Systems and
ISO 19650: Organization and Digitization of Information about Buildings and Civil Engineering Works,
including BIM, also provide guidelines and best practices for incorporating health and safety
considerations within the construction process
These standards demonstrate the recognition of BIM methodology in enhancing safety practices within the
construction industry.
A7. Table 1 lists the limitations of BIM-based safety mentioned in the articles. The limitations were categorized
according to the field of intervention mentioned earlier in Figure 5 Safety Fields Targeted by Authors. In
each category the authors listed the limitations in order of occurrence. These limitations highlight the
challenges in fully leveraging BIM for safety management in construction projects. Overcoming these
limitations will require further research and development to enhance the capabilities of BIM tools and address
the specific needs of safety planning, visualization, monitoring, automated rule checking, risk assessment,
and safety training.
Table 1 List of BIM Limitations
Safety Management
BIM Model: BIM models require manual input, leading to time-consuming errors and a lack of specific risk details. Limited case studies validate their
effectiveness. (Choe & Leite, 2017; K. Kim et al., 2016; C. S. Park & Kim, 2013; Pham et al., 2020; S. Zhang, Boukamp, et al., 2015).
GPS Integration: Low GPS accuracy hampers safety management, and integration with GIS is limited, hindering real-time updates.
Untraceable Human Behavior and Site Changes: Monitoring behavior and site changes is difficult, impacting the analysis and mitigation of safety risks.
(Choe & Leite, 2017; Golovina et al., 2016; Hosseini & Maghrebi, 2021; M. S. Khan et al., 2021; C. S. Park & Kim, 2013; S. Zhang, Boukamp, et al., 2015; S.
Zhang, Teizer, et al., 2015).
Interoperability and Frameworks: Interoperability issues between software hinder data exchange. IFC functionality may be limited, and safety regulations
may not align with specific frameworks. (Mirahadi et al., 2019; Pham et al., 2020; Y. Zhang et al., 2021)..
Visualization
BIM models lack realism, detail, and automation, relying on human visualization for risk detection and requiring a steep learning curve (Arslan et al., 2019;
BuHamdan et al., 2021; Choe & Leite, 2017; Getuli et al., 2021; Golovina et al., 2016; Hu & Zhang, 2011; Kim et al., 2016; Li et al., 2015; Mirahadi et al.,
2019; Moon et al., 2014; Park & Kim, 2013; Pham et al., 2020; Shen & Marks, 2016).
GPS integration and real-time updates face challenges, including privacy concerns, low accuracy, and limited tracking of material, worker, and work zone
movements (Arslan et al., 2019; Bansal, 2011; Fan et al., 2021; Golovina et al., 2016; Hosseini & Maghrebi, 2021; Khan et al., 2021; Kim et al., 2016; Park &
Kim, 2013; Wang et al., 2015; Zhang et al., 2015).
Comprehensive case studies are needed to validate risk detection methods, as existing studies tend to be specific and lack coverage of different
visualization models (Al Hattab et al., 2018; Kim et al., 2016; Lee et al., 2019; Wang et al., 2015).
The IFC data model and interoperability between information models and systems have limitations in addressing various risks (Mirahadi et al., 2019;
Zhang et al., 2021).
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Monitoring
BIM model and knowledge issues: Implementing new monitoring systems and acquiring the necessary knowledge is time-consuming. Limited availability
of immersive technology and comprehensive libraries restricts its application.
Tracking equipment and material: Inadequate tracking of equipment and material hampers efficient monitoring.
Automation and data analysis: Lack of automation in scheduling, zone definition, and material/equipment identification hinders data extraction, anomaly
detection, and behavior pattern identification.
Device issues: Privacy concerns, imprecise measurement, low reliability/accuracy, limited functionality, short battery life, and
communication/storage/processing limitations affect device performance.
Limited case studies and experimentation: Insufficient evidence on monitoring interventions' practicality and effectiveness in construction sites.
Lack of standards and regulations: Absence of guidelines and regulations for monitoring systems and sensor usage in health and safety.
Automated Rule Checking
BIM model limitations: Inaccurate, incomplete, or incorrect information in BIM models hinders accurate modelling and scheduling. Safety design elements
and natural constraints of construction sites are difficult to model accurately (M. A. Hossain et al., 2018; M. M. Hossain & Ahmed, 2019; Ji & Leite, 2018;
Khan et al., 2019; Malekitabar et al., 2016; Shen & Marks, 2016a; Yuan et al., 2019; S. Zhang et al., 2013; S. Zhang, Boukamp, et al., 2015; S. Zhang,
Sulankivi, et al., 2015; Zhou et al., 2021).
Safety risks and databases: Insufficient risks databases and libraries, targeting only a few risks. Increased automation is needed for risk detection in
multiple scenarios, including environmental and spatial analysis. Detection of dynamic safety hazards is lacking. Manual efforts are required to translate
safety rules into computer language and develop complex algorithms for refining safety risks. Integration of safety regulations and standards into model
checkers is still lacking (M. A. Hossain et al., 2018; M. Li et al., 2018; Rodrigues et al., 2021; Wang et al., 2015; S. Zhang et al., 2013; Zhou et al., 2021).
Complex case studies: Limited availability of complex case studies that cover a wide range of risks and construction site typologies (M. M. Hossain &
Ahmed, 2019; B. Li et al., 2022; Malekitabar et al., 2016; Wang et al., 2015; S. Zhang et al., 2013; S. Zhang, Sulankivi, et al., 2015).
Steep learning curve: Difficulty in understanding and extracting information from Automated Rule Checking tools and procedures, making it challenging
for some workers. Visualization of Automated Rule Checking information is also limited, highlighting the need for immersive tools to facilitate training,
education, and visualization (M. M. Hossain & Ahmed, 2019; Khan et al., 2019; Shen & Marks, 2016a).
Interoperability and IFC: Interoperability issues with the use of IFC as the preferred data exchange format. Expansion of IFC to target more types of risks is
necessary (S. Zhang, Boukamp, et al., 2015; S. Zhang, Sulankivi, et al., 2015; Y. Zhang et al., 2021).
Lack of standards and regulations: Limited verification of compliance with construction safety regulations. Many rules are not verifiable due to limitations
in software tools, modelling practices, or the nature of the rules themselves (M. M. Hossain & Ahmed, 2019; Yuan et al., 2019; S. Zhang et al., 2013).
Safety Planning
Automation: Safety planning relies heavily on manual efforts to analyze models and schedules, resulting in time-consuming processes (Choe & Leite, 2017;
Hosseini & Maghrebi, 2021; Kim et al., 2016a; Pham et al., 2020).
Lack of Standards and Regulations: Existing regulations and standards for general construction activities do not consider specific work or object
information, limiting their applicability in safety planning (Pham et al., 2020).
GPS and Real-time Updates: Precise tracking and accurate site location data, along with uninterrupted real-time updates, are crucial for effective safety
planning (Choe & Leite, 2017; Golovina et al., 2016; Hosseini & Maghrebi, 2021; M. S. Khan et al., 2021; Kim et al., 2016a; Park & Kim, 2013; S. Zhang,
Teizer, et al., 2015c).
Case Studies: Validation of safety planning applications through diverse case studies is necessary to assess performance in different construction sites and
risk scenarios (Kim et al., 2016a; B. Li et al., 2022; Park & Kim, 2013; Wetzel & Thabet, 2015).
BIM Models: Limitations in modeling and detailing safety-related risks restrict the effectiveness of safety planning (Choe & Leite, 2017; Park & Kim, 2013;
S. Zhang, Boukamp, et al., 2015b).
Interoperability and IFC: The transfer of simulation software application data is limited, and the use of the IFC data model for risk identification is
constrained to specific risks (Mirahadi et al., 2019; S. Zhang, Boukamp, et al., 2015b; Y. Zhang et al., 2021).
Risk Assessment
BIM Models: Low accuracy and level of detail, limited consideration of risk-related design elements, and inability to assess all activities contribute to
challenges in risk assessment (BuHamdan et al., 2021; Jin et al., 2019; P. Li et al., 2022; Liang & Liu, 2022; Malekitabar et al., 2016; Tran et al., 2021; Wang
et al., 2015b).
Case Studies: Validation of risk assessment results through diverse case studies across different countries, risks, and model characteristics is essential (B. Li
et al., 2022; P. Li et al., 2022; Liu et al., 2021; Lu et al., 2021; Malekitabar et al., 2016; Tixier et al., 2017; Tran et al., 2021; Wang et al., 2015b).
GPS and Real-time Updates: Challenges arise in incorporating real-time activities and site organization for risk assessment due to issues with sensor
reliability, power loss, limited features, and privacy concerns (Hosseini & Maghrebi, 2021; Riaz et al., 2014; Tian et al., 2021; Tixier et al., 2017; J. Zhang et
al., 2022).
Automation is Needed: Automated scanning and processing of real-time data for distinguishing risk-related activities, updating BIM models, and reducing
reliance on manual efforts can improve risk assessment (Hosseini & Maghrebi, 2021; Liu et al., 2021; Riaz et al., 2014; Tixier et al., 2017; J. Zhang et al.,
2022).
Lack of Standards and Regulations: Compatibility issues exist between automation and converting safety standards and regulations into computer-
readable language. More comprehensive safety databases and best practices are required (BuHamdan et al., 2021; Tran et al., 2021).
Safety Training and
Education
BIM Models: Inconsistent level of detail, high time and cost requirements, lack of safety libraries, and limited realism pose challenges in safety training and
education (Afzal & Shafiq, 2021; Ahn et al., 2020; Getuli, Capone, & Bruttini, 2021; H. Li et al., 2015).
Tools: Implementation of safety training tools requires acceptance from clients or managers, knowledge of BIM and VR, and overcoming issues related to
bugs, equipment costs, and complexity (Afzal & Shafiq, 2021; Clevenger et al., 2015; Getuli, Capone, & Bruttini, 2021).
Lack of Standards and Regulations: Safety training and education should align with established standards and regulations (Ahn et al., 2020).
Interoperability: Challenges in integrating safety training games with various programs and platforms hinder their effectiveness (Clevenger et al., 2015).
Case Studies: More case studies involving multiple stakeholders, diverse projects, and construction sites are needed to enhance safety training and
education (Getuli, Capone, & Bruttini, 2021; H. Li et al., 2015).
Inspection
Case Studies: Limited sample size, construction sites and projects (Alizadehsalehi et al., 2020; Park & Kim, 2013).
Sensors and Equipment: Reliability, privacy concerns, real-time updates, battery life, and GPS tracking issues pose challenges in inspection processes
(Alizadehsalehi et al., 2020; D. Liu et al., 2020; Park & Kim, 2013).
BIM Models: Insufficient modeling of safety-related details and limited coverage of risks result in time and cost-consuming inspections (Alizadehsalehi et
al., 2020; Park & Kim, 2013).
Lack of Standards and Regulations and Automation: Incompatibility between rules and standards and their conversion into computer-readable language,
as well as the need for automated analysis of tracking devices and sensors, impede inspection efforts (Alizadehsalehi et al., 2020; D. Liu et al., 2020).
A8. The targeted groups found in the current systematic review are eight categories (Figure 7). Safety managers
were mainly the focus group, and the authors developed a comprehensive set of tools specifically tailored to
assist safety managers in managing and planning health and safety strategies for the construction site. These
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tools encompass functionalities such as risk assessment, incident reporting, safety training, and compliance
monitoring. Construction managers were provided with tools that enable them to effectively monitor and
assess the site and workers' conditions in real-time, ensuring proactive identification and mitigation of
potential hazards. For designers, automated rule checking tools were developed to aid in identifying risks at
the design stage. Additionally, these tools facilitate effective communication and visualization of BIM
models, allowing for better collaboration among stakeholders and reporting of health and safety measures.
The tools available for workers and engineers on site were limited, primarily focusing on monitoring,
inspection, and training to promote a safety-conscious environment. Furthermore, facility managers were
equipped with tools for safety planning and management, enabling them to stay up-to-date with the current
site situation and ensure compliance with health and safety regulations. Students also played a role in the
research, participating in tool testing and validation before their implementation onsite. Lastly, the authors
developed safety management and visualization tools for owners to enhance their understanding of site
conditions and facilitate informed decision-making.
A9. The number of the main research dimensions of BIM are mentioned in Figure 9. The three main dimensions
commonly used in BIM are:
3D Model: The 3D model enables visualization, clash detection, and spatial coordination among different
disciplines.
4D Simulation: It aids in project planning, construction phasing, and visualizing the project's progression
over time.
5D Estimation: The addition of cost-related information to the 3D model enables cost estimation, quantity
take-off, and budget control.
These findings suggest that, for most health and safety solutions, 4D models are the ideal choice, while 3D
models are sufficient for general visualization, collision detection, and model walkthroughs. The results also
highlight a lack of diversity in exploring BIM dimensions within the health and safety field, which may
represent missed opportunities for certain research areas to fully leverage BIM's potential. Additionally, the
findings demonstrate a misconception and confusion regarding BIM dimensions, as studies related to PtD
should have referred to 8D BIM concepts, and if cost-related information is included, the model should be
described as 5D.
BIM LODs define the level of detail and accuracy of the elements within a BIM model at different project
stages. The American Institute of Architects (AIA) introduced a widely accepted LOD specification that ranges
from LOD 100 (conceptual design) to LOD 500 (as-built condition). LODs provide a common language for
communicating the expected level of development for different project components.
Prior to the introduction of LOIN ISO, BIM requirements varied across projects and jurisdictions, leading to
inconsistencies and interoperability challenges. However, the publication of the ISO 19650 series on BIM
standards in 2018 provided a standardized approach to information management throughout the asset lifecycle
(BSI, 2019). The LOIN ISO (ISO 19650-2) specifically focuses on defining the information requirements,
enabling effective information exchange and facilitating data interoperability.
4.2 Remarks on BIM for health and safety “Limitations and solutions”
BIM offers numerous benefits for health and safety management in construction, but it also faces limitations that
hinder its full potential. One limitation is the inadequate integration of health and safety parameters in BIM
models, which focus primarily on design and construction rather than safety considerations. This limits BIM's
ability to proactively address safety hazards and account for site-specific factors like temporary structures or
climatic variations.
Another limitation is the lack of standardization and interoperability among different BIM platforms and software
applications. This results in data fragmentation and hampers collaboration among project teams. The absence of
standardized health and safety parameters makes it difficult to capture and analyse safety-related data
consistently.
The adoption of BIM for health and safety management also faces technological and training barriers.
Construction professionals may lack the necessary technical skills and knowledge to effectively use BIM tools.
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Additionally, the reliance on advanced technologies like virtual reality or augmented reality requires significant
investments in infrastructure, posing financial barriers for smaller firms.
To overcome these limitations, several solutions can be implemented. Enhancing the integration of health and
safety parameters within BIM models can be achieved by developing standardized libraries of safety-related
elements and simulating real-time construction site conditions. Integrating sensors and IoT devices with BIM
models can provide real-time data on environmental conditions, worker movements, and equipment operations
for improved safety assessments.
Addressing the issue of limited standardization and interoperability requires industry-wide initiatives to establish
common protocols and data exchange formats. Open standards like Industry Foundation Classes (IFC) can
facilitate interoperability, and the development of APIs can integrate specialized safety analysis tools with BIM
software.
Technological advancements should focus on user-friendly interfaces and intuitive tools tailored for health and
safety management within BIM software. Investing in training and education programs is crucial to enhance the
technical skills and knowledge of construction professionals in utilising BIM for safety purposes.
Integrating augmented reality (AR) with BIM can provide real-time safety visualisation on construction sites.
AR overlays safety-related information onto the physical environment, improving situational awareness and
supporting real-time decision-making.
In conclusion, addressing the limitations of BIM for health and safety management requires enhancing
integration, improving standardisation and interoperability, and overcoming technological and training barriers.
By implementing solutions such as standardised libraries, real-time simulation, open standards, APIs, user-
friendly interfaces, and augmented reality, BIM can be optimised to enhance safety practices and mitigate risks
in the construction sector.
4.3 Limitations of the Systematic Review
While this systematic review offers valuable insights into the use of digital technologies in the construction
sector, it is important to consider its limitations and potential biases that may have influenced the findings.
Firstly, the review's inclusion of articles is not extensive due to the deliberate focus on a specific scope defined
by inclusion and exclusion criteria. While this focused approach enhances relevance, it may limit the overall
comprehensiveness. Including a larger number of articles could provide a more comprehensive overview.
Secondly, the quality level of the included studies varies. Despite careful consideration of quality criteria,
subjective interpretation may have led to the improper exclusion or inclusion of some articles. This potential bias
should be acknowledged when assessing the robustness of the results. Future reviews could employ a more
rigorous selection process to minimize biases.
Additionally, the review's narrow focus on the construction sector means contributions from other fields, even if
they involve the same digital technologies, were excluded. While this sector-specific approach allows for a deeper
examination of the construction industry, it limits the generalizability of the findings to other industries or sectors.
Exploring implications in other domains is crucial for a broader understanding.
Moreover, this review is subject to the limitations inherent in the selected studies themselves. The quality,
methodology, and reporting standards of the primary studies can vary, introducing bias and affecting the overall
reliability of the findings. Careful consideration of these limitations is necessary when interpreting the
conclusions.
Acknowledging these limitations helps maintain a balanced perspective on the findings. Future research should
aim to include a larger number of high-quality studies from diverse fields, adopting more rigorous methodologies.
This would provide a more comprehensive and reliable understanding of the implications and potential of digital
technologies across various sectors.”
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4.4 Linking the Theoretical Causation and prevention model to new digital technologies framework.
In order to effectively leverage new digital technologies, such as BIM and other digital tools, within the
construction industry, it is essential to establish a framework that connects these technologies with a theoretical
causation and prevention model. This framework aims to enhance the understanding and implementation of
safety practices, as well as facilitate the integration of digital tools for accident prevention and improved safety
outcomes. The following components are proposed for this framework:
1. Theoretical Causation Model:
The first step involves developing or selecting an appropriate theoretical causation model that explains the
factors contributing to accidents and safety incidents in the construction industry. This model should consider
various aspects, such as human factors, organizational factors, environmental factors, and technological factors.
It should provide a comprehensive understanding of the causal relationships between these factors and safety
outcomes.
2. Digital Technologies Integration:
The next step is to identify and integrate relevant digital technologies into the existing safety practices and
processes. BIM, AR, VR, IoT, and wearable devices are examples of digital technologies that can be utilized.
The integration process should consider compatibility with existing systems, training requirements, and data
management strategies.
3. Data Collection and Analysis:
Once the digital technologies are implemented, it is crucial to establish mechanisms for data collection and
analysis. This includes capturing real-time data from the construction site, such as workers' activities, equipment
usage, and environmental conditions. The data should be systematically organized, standardized, and stored in a
secure manner to ensure its reliability and accessibility.
4. Linking Data to Causation Model:
The collected data should be linked to the previously established theoretical causation model. By analyzing the
data in the context of the model, potential risk factors, patterns, and correlations can be identified. This step
enables a deeper understanding of the underlying causes of accidents and safety incidents.
5. Risk Assessment and Prevention Strategies:
Based on the findings from the data analysis, risk assessment can be conducted to evaluate the potential hazards
and risks associated with specific activities or areas in the construction site. Prevention strategies can then be
developed and implemented, taking into account the insights gained from the causation model and the capabilities
of the digital technologies.
6. Continuous Monitoring and Feedback Loop:
Digital technologies enable real-time monitoring of the construction site, allowing stakeholders to proactively
identify potential safety issues and intervene before accidents occur. The continuous monitoring process should
be complemented with feedback loops that facilitate communication and collaboration among project
stakeholders, enabling them to respond promptly to safety concerns and improve safety practices.
7. Evaluation and Iterative Improvement:
The final component of the framework involves evaluating the effectiveness of the integrated digital
technologies and prevention strategies. This evaluation should encompass safety outcomes, efficiency gains,
worker satisfaction, and other relevant metrics. The insights gained from the evaluation process should inform
iterative improvements to the framework and its implementation in future projects.
By adopting this framework, stakeholders in the construction industry can effectively leverage new digital
technologies to enhance safety practices, prevent accidents, and improve overall project performance. The
framework provides a systematic approach to link theoretical causation models with practical implementation
strategies, fostering a safer working environment and facilitating continuous improvement in construction safety.
Conclusions
The systematic review provides compelling evidence for the effectiveness of Building Information Modeling
(BIM) and digital tools in improving health and safety practices within the construction industry. The findings
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demonstrate that the adoption of these technologies can lead to significant improvements in safety outcomes,
including a reduction in accidents and enhanced overall project performance.
By leveraging BIM and digital technologies, construction professionals can benefit from proactive safety
measures, real-time monitoring, and enhanced incident management capabilities. These technologies facilitate
efficient hazard identification, risk assessment, safety planning, and response mechanisms, enabling stakeholders
to prevent accidents and ensure a safer working environment.
However, it is important to acknowledge the limitations of the current literature. The included studies varied in
methodologies, sample sizes, and contextual factors, which may limit the generalizability of the findings.
Additionally, there may be potential biases and gaps in the existing research, underscoring the need for further
investigation.
To advance the field, future research should focus on exploring specific applications of digital technologies
within the construction industry, evaluating long-term impacts, and identifying potential barriers to
implementation. Efforts should also be made to address data availability and reporting quality to strengthen the
evidence base.
In conclusion, the systematic review supports the integration of BIM and digital tools as a valuable approach for
enhancing construction safety. By linking theoretical causation models with these technologies, stakeholders can
leverage their full potential to prevent accidents, improve safety practices, and ultimately create safer work
environments in the construction industry.
References
Abdelhamid, T. S., & Everett, J. G. (2000). Identifying Root Causes of Construction Accidents. Journal of Construction
Engineering and Management, 126(1), 52–60. https://doi.org/10.1061/(asce)0733-9364(2000)126:1(52)
Abed, H. R., Hatem, W. A., & Jasim, N. A. (2019). Adopting BIM Technology in Fall Prevention Plans. Civil Engineering Journal,
5(10), 2270–2281. https://doi.org/10.28991/cej-2019-03091410
Afzal, M., & Shafiq, M. T. (2021). Evaluating 4d-bim and vr for effective safety communication and training: A case study of
multilingual construction job-site crew. Buildings, 11(8). https://doi.org/10.3390/buildings11080319
Ahn, S., Kim, T., Park, Y. J., & Kim, J. M. (2020). Improving Effectiveness of Safety Training at Construction Worksite Using 3D
BIM Simulation. Advances in Civil Engineering, 2020. https://doi.org/10.1155/2020/2473138
Akula, M., Lipman, R. R., Franaszek, M., Saidi, K. S., Cheok, G. S., & Kamat, V. R. (2013). Real-time drill monitoring and control
using building information models augmented with 3D imaging data. Automation in Construction, 36, 1–15.
https://doi.org/10.1016/j.autcon.2013.08.010
Al Hattab, M., Zankoul, E., Barakat, M., & Hamzeh, F. (2018). Crane overlap and operational flexibility: Balancing utilization,
duration, and safety. Construction Innovation, 18(1), 43–63. https://doi.org/10.1108/CI-11-2016-0062
Alizadehsalehi, S., Yitmen, I., Celik, T., & Arditi, D. (2020). The effectiveness of an integrated BIM/UAV model in managing
safety on construction sites. International Journal of Occupational Safety and Ergonomics, 26(4), 829–844.
https://doi.org/10.1080/10803548.2018.1504487
Arslan, M., Cruz, C., & Ginhac, D. (2019a). Semantic trajectory insights for worker safety in dynamic environments.
Automation in Construction, 106, 102854. https://doi.org/10.1016/j.autcon.2019.102854
Arslan, M., Cruz, C., & Ginhac, D. (2019b). Semantic enrichment of spatio-temporal trajectories for worker safety on
construction sites. Personal and Ubiquitous Computing, 23(5–6), 749–764. https://doi.org/10.1007/s00779-018-01199-
5
Arslan, M., Cruz, C., & Ginhac, D. (2019c). Visualizing intrusions in dynamic building environments for worker safety. Safety
Science, 120, 428–446. https://doi.org/10.1016/j.ssci.2019.07.020
Arslan, M., Riaz, Z., Kiani, A. K., & Azhar, S. (2014). Real-time environmental monitoring, visualization and notification system
for construction H&S management. Journal of Information Technology in Construction, 19, 72–91.
https://doi.org/10.5840/agstm201454111
Bansal, V. K. (2011). Application of geographic information systems in construction safety planning. International Journal of
This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=4595455
Preprint not peer reviewed

Project Management, 29(1), 66–77. https://doi.org/10.1016/j.ijproman.2010.01.007
Bao, Q., Zhou, J., Zhao, Y., Li, X., Tao, S., & Duan, P. (2022). Developing A Rule-Based Dynamic Safety Checking Method for
Enhancing Construction Safety. Sustainability 2022, Vol. 14, Page 14130, 14(21), 14130.
https://doi.org/10.3390/SU142114130
Bellamy, L. J. (2015). Exploring the relationship between major hazard, fatal and non-fatal accidents through outcomes and
causes. Safety Science, 71(PB), 93–103. https://doi.org/10.1016/j.ssci.2014.02.009
Bhagwat, K., Kumar, P., & Delhi, V. S. K. (2021). Usability of Visualization Platform–Based Safety Training and Assessment
Modules for Engineering Students and Construction Professionals. Journal of Civil Engineering Education, 147(2),
04020016. https://doi.org/10.1061/(asce)ei.2643-9115.0000034
Bird, J. E. . (1990). Practical loss control leadership, Loganvile. International Loss Control Institute.
https://books.google.pt/books?id=Cg73QwAACAAJ
BuHamdan, S., Duncheva, T., & Alwisy, A. (2021). Developing a BIM and Simulation-Based Hazard Assessment and
Visualization Framework for CLT Construction Design. Journal of Construction Engineering and Management, 147(3),
04021003. https://doi.org/10.1061/(asce)co.1943-7862.0002000
Charef, R., Alaka, H., & Emmitt, S. (2018). Beyond the third dimension of BIM: A systematic review of literature and
assessment of professional views. Journal of Building Engineering, 19, 242–257.
https://doi.org/10.1016/j.jobe.2018.04.028
Cheung, W. F., Lin, T. H., & Lin, Y. C. (2018). A real-time construction safety monitoring system for hazardous gas integrating
wireless sensor network and building information modeling technologies. Sensors (Switzerland), 18(2), 436.
https://doi.org/10.3390/s18020436
Choe, S., & Leite, F. (2017). Construction safety planning: Site-specific temporal and spatial information integration.
Automation in Construction, 84, 335–344. https://doi.org/10.1016/j.autcon.2017.09.007
Clevenger, C., Del Puerto, C. L., & Glick, S. (2015). Interactive BIM-enabled safety training piloted in construction education.
Advances in Engineering Education, 4(3).
Collado-Mariscal, D., Cortés-Pérez, J. P., Cortés-Pérez, A., & Cuevas-Murillo, A. (2022). Proposal for the Integration of Health
and Safety into the Design of Road Projects with BIM. Buildings 2022, Vol. 12, Page 1753, 12(10), 1753.
https://doi.org/10.3390/BUILDINGS12101753
Collinge, W. H., Farghaly, K., Mosleh, M. H., Manu, P., Cheung, C. M., & Osorio-Sandoval, C. A. (2022). BIM-based construction
safety risk library. Automation in Construction, 141. https://doi.org/10.1016/J.AUTCON.2022.104391
Cortés-Pérez, J. P., Cortés-Pérez, A., & Prieto-Muriel, P. (2020). BIM-integrated management of occupational hazards in
building construction and maintenance. Automation in Construction, 113, 103115.
https://doi.org/10.1016/j.autcon.2020.103115
Costin, A. M., Teizer, J., & Schoner, B. (2015). RFID and bim-enabled worker location tracking to support real-time building
protocol control and data visualization. Journal of Information Technology in Construction, 20(29), 495–517.
http://www.itcon.org/paper/2015/29
Daniotti, B., Pavan, A., Lupica Spagnolo, S., Caffi, V., Pasini, D., & Mirarchi, C. (2020). BIM-Based Collaborative Building
Process Management. Springer International Publishing. https://doi.org/10.1007/978-3-030-32889-4
Deng, L., Zhong, M., Liao, L., Peng, L., & Lai, S. (2019). Research on safety management application of dangerous sources in
engineering construction based on bim technology. Advances in Civil Engineering, 2019.
https://doi.org/10.1155/2019/7450426
Ding, L. Y., Zhong, B. T., Wu, S., & Luo, H. B. (2016). Construction risk knowledge management in BIM using ontology and
semantic web technology. Safety Science, 87, 202–213. https://doi.org/10.1016/j.ssci.2016.04.008
Dong, S., Li, H., & Yin, Q. (2018). Building information modeling in combination with real time location systems and sensors
for safety performance enhancement. Safety Science, 102, 226–237. https://doi.org/10.1016/j.ssci.2017.10.011
Eastman, C., Lee, J. kook J. min, Jeong, Y. suk, & Lee, J. kook J. min. (2009). Automatic rule-based checking of building designs.
Automation in Construction, 18(8), 1011–1033. https://doi.org/10.1016/j.autcon.2009.07.002
This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=4595455
Preprint not peer reviewed

Farghaly, K., Soman, R. K., Collinge, W., Mosleh, M. H., Manu, P., & Cheung, C. M. (2022). Construction Safety Ontology
Development and Alignment With Industry Foundation Classes (Ifc). Journal of Information Technology in Construction,
27, 94–108. https://doi.org/10.36680/j.itcon.2022.005
Gerhard, T. (2008). Bias: Considerations for research practice. In American Journal of Health-System Pharmacy (Vol. 65, Issue
22, pp. 2159–2168). Narnia. https://doi.org/10.2146/ajhp070369
Getuli, V., Capone, P., & Bruttini, A. (2021). Planning, management and administration of HS contents with BIM and VR in
construction: an implementation protocol. Engineering, Construction and Architectural Management, 28(2), 603–623.
https://doi.org/10.1108/ECAM-11-2019-0647
Getuli, V., Capone, P., Bruttini, A., & Isaac, S. (2020). BIM-based immersive Virtual Reality for construction workspace
planning: A safety-oriented approach. Automation in Construction, 114. https://doi.org/10.1016/j.autcon.2020.103160
Getuli, V., Capone, P., Bruttini, A., & Sorbi, T. (2021). A smart objects library for BIM-based construction site and emergency
management to support mobile VR safety training experiences. Construction Innovation. https://doi.org/10.1108/CI-04-
2021-0062
Golovina, O., Perschewski, M., Teizer, J., & König, M. (2019). Algorithm for quantitative analysis of close call events and
personalized feedback in construction safety. Automation in Construction, 99, 206–222.
https://doi.org/10.1016/j.autcon.2018.11.014
Golovina, O., Teizer, J., & Pradhananga, N. (2016). Heat map generation for predictive safety planning: Preventing struck-by
and near miss interactions between workers-on-foot and construction equipment. Automation in Construction, 71, 99–
115. https://doi.org/10.1016/j.autcon.2016.03.008
Hara, T., Shimomura, K., Hamano, K., & Miyake, S. (2019). Automatic design, planning and drawing of scaffolding system for
constructions. Advances in Computational Design, 4(2), 179–196. https://doi.org/10.12989/acd.2019.4.2.179
Hasan, S. M., Lee, K., Moon, D., Kwon, S., Jinwoo, S., & Lee, S. (2022). Augmented reality and digital twin system for
interaction with construction machinery. Journal of Asian Architecture and Building Engineering, 21(2), 564–574.
https://doi.org/10.1080/13467581.2020.1869557
Herzanita, A., Latief, Y., & Lestari, F. (2022). The Application of BIM-Based OHSMS Information Systems to Improve Safety
Performance. International Journal of Safety and Security Engineering, 12(1), 31–38.
https://doi.org/10.18280/IJSSE.120104
Hossain, M. A., Abbott, E. L. S., Chua, D. K. H., Nguyen, T. Q., & Goh, Y. M. (2018). Design-for-Safety knowledge library for
BIM-integrated safety risk reviews. Automation in Construction, 94, 290–302.
https://doi.org/10.1016/j.autcon.2018.07.010
Hossain, M. M., & Ahmed, S. (2019). Developing an automated safety checking system using BIM: a case study in the
Bangladeshi construction industry. International Journal of Construction Management.
https://doi.org/10.1080/15623599.2019.1686833
Hosseini, O., & Maghrebi, M. (2021). Risk of fire emergency evacuation in complex construction sites: Integration of 4D-BIM,
social force modeling, and fire quantitative risk assessment. Advanced Engineering Informatics, 50.
https://doi.org/10.1016/j.aei.2021.101378
Hu, Z., & Zhang, J. (2011). BIM- and 4D-based integrated solution of analysis and management for conflicts and structural
safety problems during construction: 2. Development and site trials. Automation in Construction, 20(2), 155–166.
https://doi.org/10.1016/j.autcon.2010.09.013
Ji, Y., & Leite, F. (2018). Automated tower crane planning: leveraging 4-dimensional BIM and rule-based checking.
Automation in Construction, 93, 78–90. https://doi.org/10.1016/j.autcon.2018.05.003
Kamardeen, I. (2010). 8D BIM modelling tool for accident prevention through design. Association of Researchers in
Construction Management, ARCOM 2010 - Proceedings of the 26th Annual Conference, 281–289.
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.461.8274&rep=rep1&type=pdf
Khan, M. S., Park, J., & Seo, J. (2021). Geotechnical property modeling and construction safety zoning based on gis and bim
integration. Applied Sciences (Switzerland), 11(9). https://doi.org/10.3390/app11094004
Khan, N., Ali, A. K., Skibniewski, M. J., Lee, D. Y., & Park, C. (2019). Excavation Safety Modeling Approach Using BIM and VPL.
This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=4595455
Preprint not peer reviewed

Advances in Civil Engineering, 2019. https://doi.org/10.1155/2019/1515808
Kim, H., Lee, H.-S., Park, M., Chung, B., & Hwang, S. (2015). Information Retrieval Framework for Hazard Identification in
Construction. Journal of Computing in Civil Engineering, 29(3), 04014052. https://doi.org/10.1061/(asce)cp.1943-
5487.0000340
Kim, H., Lee, H. S., Park, M., Chung, B. Y., & Hwang, S. (2016). Automated hazardous area identification using laborers’ actual
and optimal routes. Automation in Construction, 65, 21–32. https://doi.org/10.1016/j.autcon.2016.01.006
Kim, K., Cho, Y. K., & Kim, K. (2018a). BIM-Based Decision-Making Framework for Scaffolding Planning. Journal of
Management in Engineering, 34(6), 04018046. https://doi.org/10.1061/(asce)me.1943-5479.0000656
Kim, K., Cho, Y., & Kim, K. (2018b). BIM-Driven Automated Decision Support System for Safety Planning of Temporary
Structures. Journal of Construction Engineering and Management, 144(8), 04018072.
https://doi.org/10.1061/(asce)co.1943-7862.0001519
Kim, K., Cho, Y., & Zhang, S. (2016). Integrating work sequences and temporary structures into safety planning: Automated
scaffolding-related safety hazard identification and prevention in BIM. Automation in Construction, 70, 128–142.
https://doi.org/10.1016/j.autcon.2016.06.012
Labor, U. S. D. of. (2019). National Census Of Fatal Occupational Injuries In 2019. In Bureau of Labor Statistics (Issue 202).
https://www.bls.gov/news.release/pdf/cfoi.pdf
Lee, P. C., Wei, J., Ting, H. I., Lo, T. P., Long, D., & Chang, L. M. (2019). Dynamic Analysis of Construction Safety Risk and Visual
Tracking of Key Factors based on Behavior-based Safety and Building Information Modeling. KSCE Journal of Civil
Engineering, 23(10), 4155–4167. https://doi.org/10.1007/s12205-019-0283-z
Li, B., Schultz, C., Teizer, J., Golovina, O., & Melzner, J. (2022). Towards a unifying domain model of construction safety,
health and well-being: SafeConDM. Advanced Engineering Informatics, 51. https://doi.org/10.1016/j.aei.2021.101487
Li, H., Lu, M., Chan, G., & Skitmore, M. (2015). Proactive training system for safe and efficient precast installation.
Automation in Construction, 49(PA), 163–174. https://doi.org/10.1016/j.autcon.2014.10.010
Li, H., Lu, M., Hsu, S. C., Gray, M., & Huang, T. (2015). Proactive behavior-based safety management for construction safety
improvement. Safety Science, 75, 107–117. https://doi.org/10.1016/j.ssci.2015.01.013
Li, M., Yu, H., & Liu, P. (2018). An automated safety risk recognition mechanism for underground construction at the pre-
construction stage based on BIM. Automation in Construction, 91, 284–292.
https://doi.org/10.1016/j.autcon.2018.03.013
Li, P., Wang, Q., Guo, Z., Mei, T., Li, Q., Qiao, S., & Zuo, W. (2022). Identifying Falling-from-Height Hazards in Building
Information Models: A Voxelization-Based Method. Journal of Construction Engineering and Management, 148(2).
https://doi.org/10.1061/(asce)co.1943-7862.0002236
Li, X., Yang, D., Yuan, J., Donkers, A., & Liu, X. (2022). BIM-enabled semantic web for automated safety checks in subway
construction. Automation in Construction, 141. https://doi.org/10.1016/J.AUTCON.2022.104454
Liang, Y., & Liu, Q. (2022). Early warning and real-time control of construction safety risk of underground engineering based
on building information modeling and internet of things. Neural Computing and Applications, 34(5), 3433–3442.
https://doi.org/10.1007/s00521-021-05755-8
Liu, D., Jin, Z., & Gambatese, J. (2020). Scenarios for Integrating IPS–IMU System with BIM Technology in Construction Safety
Control. Practice Periodical on Structural Design and Construction, 25(1), 05019007.
https://doi.org/10.1061/(asce)sc.1943-5576.0000465
Liu, P. (2021). Mountain Rainfall Estimation and BIM Technology Site Safety Management Based on Internet of Things. Mobile
Information Systems, 2021. https://doi.org/10.1155/2021/1017200
Liu, P. (2022). Research on Site Safety Management by BIM Technology Based on Fuzzy Intuition Set. Security and
Communication Networks, 2022. https://doi.org/10.1155/2022/7289440
Liu, Z., Meng, X., Xing, Z., & Jiang, A. (2021). Digital twin-based safety risk coupling of prefabricated building hoisting. Sensors,
21(11). https://doi.org/10.3390/s21113583
Lu, Y., Gong, P., Tang, Y., Sun, S., & Li, Q. (2021). BIM-integrated construction safety risk assessment at the design stage of
This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=4595455
Preprint not peer reviewed

building projects. Automation in Construction, 124. https://doi.org/10.1016/j.autcon.2021.103553
Malekitabar, H., Ardeshir, A., Sebt, M. H., & Stouffs, R. (2016). Construction safety risk drivers: A BIM approach. Safety
Science, 82, 445–455. https://doi.org/10.1016/j.ssci.2015.11.002
Marzouk, M., & Daour, I. Al. (2018). Planning labor evacuation for construction sites using BIM and agent-based simulation.
Safety Science, 109, 174–185. https://doi.org/10.1016/j.ssci.2018.04.023
Melzner, J., Zhang, S., Teizer, J., & Bargstädt, H. J. (2013). A case study on automated safety compliance checking to assist fall
protection design and planning in building information models. Construction Management and Economics, 31(6), 661–
674. https://doi.org/10.1080/01446193.2013.780662
Mihić, M., Cerić, A., & Završki, I. (2018). Developing construction Hazard database for automated hazard identification
process. Tehnicki Vjesnik, 25(6), 1761–1769. https://doi.org/10.17559/TV-20180417105624
Mirahadi, F., McCabe, B., & Shahi, A. (2019). IFC-centric performance-based evaluation of building evacuations using fire
dynamics simulation and agent-based modeling. Automation in Construction, 101, 1–16.
https://doi.org/10.1016/j.autcon.2019.01.007
Moon, H., Kim, H., Kim, C., & Kang, L. (2014). Development of a schedule-workspace interference management system
simultaneously considering the overlap level of parallel schedules and workspaces. Automation in Construction, 39, 93–
105. https://doi.org/10.1016/j.autcon.2013.06.001
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A.,
Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson,
E., McDonald, S., … Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic
reviews. In The BMJ (Vol. 372). British Medical Journal Publishing Group. https://doi.org/10.1136/bmj.n71
Pannucci, C. J., & Wilkins, E. G. (2010). Identifying and avoiding bias in research. Plastic and Reconstructive Surgery, 126(2),
619–625. https://doi.org/10.1097/PRS.0b013e3181de24bc
Park, C. S., & Kim, H. J. (2013). A framework for construction safety management and visualization system. Automation in
Construction, 33, 95–103. https://doi.org/10.1016/j.autcon.2012.09.012
Park, J., Kim, K., & Cho, Y. K. (2017). Framework of Automated Construction-Safety Monitoring Using Cloud-Enabled BIM and
BLE Mobile Tracking Sensors. Journal of Construction Engineering and Management, 143(2), 05016019.
https://doi.org/10.1061/(asce)co.1943-7862.0001223
Park, S., & Kim, I. (2015). BIM-Based quality control for safety issues in the design and construction phases. Archnet-IJAR,
9(3), 111–129. https://doi.org/10.26687/archnet-ijar.v9i3.881
Perry, P. (2016). The Construction (Design and Management) Regulations 2015 explained. Health and Safety Questions and
Answers, 55–84. https://doi.org/10.1680/hasqaa.60746.055
Pham, K. T., Vu, D. N., Hong, P. L. H., & Park, C. (2020). 4D-BIM-based workspace planning for temporary safety facilities in
construction SMES. International Journal of Environmental Research and Public Health, 17(10).
https://doi.org/10.3390/ijerph17103403
Ragnoli, M., Colaiuda, D., Leoni, A., Ferri, G., Barile, G., Rotilio, M., Laurini, E., De Berardinis, P., & Stornelli, V. (2022). A
LoRaWAN Multi-Technological Architecture for Construction Site Monitoring. Sensors, 22(22).
https://doi.org/10.3390/S22228685
Ramos-Hurtado, J., Muñoz-La Rivera, F., Mora-Serrano, J., Deraemaeker, A., & Valero, I. (2022). Proposal for the Deployment
of an Augmented Reality Tool for Construction Safety Inspection. Buildings 2022, Vol. 12, Page 500, 12(4), 500.
https://doi.org/10.3390/BUILDINGS12040500
Riaz, Z., Arslan, M., Kiani, A. K., & Azhar, S. (2014). CoSMoS: A BIM and wireless sensor based integrated solution for worker
safety in confined spaces. Automation in Construction, 45, 96–106. https://doi.org/10.1016/j.autcon.2014.05.010
Riaz, Z., Parn, E. A., Edwards, D. J., Arslan, M., Shen, C., & Pena-Mora, F. (2017). BIM and sensor-based data management
system for construction safety monitoring. Journal of Engineering, Design and Technology, 15(6), 738–753.
https://doi.org/10.1108/JEDT-03-2017-0017
Rodrigues, F., Antunes, F., & Matos, R. (2021). Safety plugins for risks prevention through design resourcing BIM. Construction
This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=4595455
Preprint not peer reviewed

Innovation, 21(2), 244–258. https://doi.org/10.1108/CI-12-2019-0147
Sampaio, A. Z., Constantino, G. B., & Almeida, N. M. (2022). 8D BIM Model in Urban Rehabilitation Projects: Enhanced
Occupational Safety for Temporary Construction Works. Applied Sciences (Switzerland), 12(20), 10577.
https://doi.org/10.3390/APP122010577/S1
Shen, Q., Wu, S., Deng, Y., Deng, H., & Cheng, J. C. P. (2022). BIM-Based Dynamic Construction Safety Rule Checking Using
Ontology and Natural Language Processing. Buildings 2022, Vol. 12, Page 564, 12(5), 564.
https://doi.org/10.3390/BUILDINGS12050564
Shen, X., & Marks, E. (2016). Near-Miss Information Visualization Tool for BIM. Construction Research Congress 2016: Old and
New Construction Technologies Converge in Historic San Juan - Proceedings of the 2016 Construction Research
Congress, CRC 2016, 142(4), 2916–2925. https://doi.org/10.1061/9780784479827.290
Shen, Y., Xu, M., Lin, Y., Cui, C., Shi, X., & Liu, Y. (2022). Safety Risk Management of Prefabricated Building Construction Based
on Ontology Technology in the BIM Environment. Buildings 2022, Vol. 12, Page 765, 12(6), 765.
https://doi.org/10.3390/BUILDINGS12060765
Sidani, A., Poças Martins, J., & Soeiro, A. (2022). BIM approaches for enhanced health and safety status in construction -
protocol for a systematic review. International Journal of Occupational and Environmental Safety, 6(1), 1–8.
https://doi.org/10.24840/2184-0954_006.001_0001
Smaoui, N., Kim, K., Gnawali, O., Lee, Y. J., & Suh, W. (2018). Respirable dust monitoring in construction sites and visualization
in building information modeling using real-time sensor data. Sensors and Materials, 30(8), 1775–1786.
https://doi.org/10.18494/SAM.2018.1871
Tak, A. N., Taghaddos, H., Mousaei, A., Bolourani, A., & Hermann, U. (2021). BIM-based 4D mobile crane simulation and
onsite operation management. Automation in Construction, 128, 103766.
https://doi.org/10.1016/j.autcon.2021.103766
Tian, W., Meng, J., Zhong, X. J., & Tan, X. (2021). Intelligent Early Warning System for Construction Safety of Excavations
Adjacent to Existing Metro Tunnels. Advances in Civil Engineering, 2021. https://doi.org/10.1155/2021/8833473
Tixier, A. J. P., Hallowell, M. R., Rajagopalan, B., & Bowman, D. (2017). Construction Safety Clash Detection: Identifying Safety
Incompatibilities among Fundamental Attributes using Data Mining. Automation in Construction, 74, 39–54.
https://doi.org/10.1016/j.autcon.2016.11.001
Tran, S. V. T., Khan, N., Lee, D., & Park, C. (2021). A hazard identification approach of integrating 4d bim and accident case
analysis of spatial–temporal exposure. Sustainability (Switzerland), 13(4), 1–19. https://doi.org/10.3390/su13042211
U.S. Department of Labor. (2019). OSHA Regulation - 3021-12R 2019 - Workers Rights - Construction Documents And
Templates. chrome-
extension://efaidnbmnnnibpcajpcglclefindmkaj/https://www.osha.gov/sites/default/files/publications/osha3021.pdf
Wang, J., Zhang, S., & Teizer, J. (2015). Geotechnical and safety protective equipment planning using range point cloud data
and rule checking in building information modeling. Automation in Construction, 49, 250–261.
https://doi.org/10.1016/j.autcon.2014.09.002
Wang, Y. (2018). The Theory of Zero Incident Safety Management. Journal of Civil, Construction and Environmental
Engineering, 3(3), 83. https://doi.org/10.11648/j.jccee.20180303.15
Wetzel, E. M., & Thabet, W. Y. (2015). The use of a BIM-based framework to support safe facility management processes.
Automation in Construction, 60, 12–24. https://doi.org/10.1016/j.autcon.2015.09.004
Williams, G., Gheisari, M., Chen, P.-J., Irizarry, J., Asce, A. M., Chen, P.-J., Irizarry, J., & Asce, M. (2015). BIM2MAR: An Efficient
BIM Translation to Mobile Augmented Reality Applications. Journal of Management in Engineering, 31(1), 481–498.
https://doi.org/10.1061/(asce)me.1943-5479.0000315
Winge, S., Albrechtsen, E., & Mostue, B. A. (2019). Causal factors and connections in construction accidents. Safety Science,
112, 130–141. https://doi.org/10.1016/j.ssci.2018.10.015
Wohlin, C. (2014). Guidelines for snowballing in systematic literature studies and a replication in software engineering. ACM
International Conference Proceeding Series. https://doi.org/10.1145/2601248.2601268
This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=4595455
Preprint not peer reviewed

Wu, I. C., Lu, S. R., & Hsiung, B. C. (2015). A BIM-based monitoring system for urban deep excavation projects. Visualization in
Engineering, 3(1), 1–11. https://doi.org/10.1186/s40327-014-0015-x
Xie, P., Zhang, R., Zheng, J., & Li, Z. (2022). AUTOMATIC SAFETY EVALUATION AND VISUALIZATION OF SUBWAY STATION
EXCAVATION BASED ON BIM-FEM/FDM INTEGRATED TECHNOLOGY. Journal of Civil Engineering and Management,
28(4), 320–336. https://doi.org/10.3846/JCEM.2022.16727
Xun, X., Zhang, J., & Yuan, Y. (2022). Multi-Information Fusion Based on BIM and Intuitionistic Fuzzy D-S Evidence Theory for
Safety Risk Assessment of Undersea Tunnel Construction Projects. Buildings, 12(11).
https://doi.org/10.3390/BUILDINGS12111802
Yang, B., Zhang, B., Zhang, Q., Wang, Z., Dong, M., & Fang, T. (2022). Automatic detection of falling hazard from surveillance
videos based on computer vision and building information modeling. Structure and Infrastructure Engineering, 1–15.
https://doi.org/10.1080/15732479.2022.2039217
Yang, Y., Sun, Y., Chen, M., Zhou, Y., Wang, R., & Liu, Z. (2022). Platform Development of BIM-Based Fire Safety Management
System Considering the Construction Site. Buildings 2022, Vol. 12, Page 1268, 12(8), 1268.
https://doi.org/10.3390/BUILDINGS12081268
Yi, S. L., Zhang, X., & Calvo, M. H. (2015). Construction safety management of building project based on BIM. Journal of
Mechanical Engineering Research and Developments, 38(1), 97–104.
Yuan, J., Li, X., Xiahou, X., Tymvios, N., Zhou, Z., & Li, Q. (2019). Accident prevention through design (PtD): Integration of
building information modeling and PtD knowledge base. Automation in Construction, 102, 86–104.
https://doi.org/10.1016/j.autcon.2019.02.015
Zhang, J., Cheng, J. C. P., Chen, W., & Chen, K. (2022). Digital Twins for Construction Sites: Concepts, LoD Definition, and
Applications. Journal of Management in Engineering, 38(2). https://doi.org/10.1061/(asce)me.1943-5479.0000948
Zhang, J. P., & Hu, Z. Z. (2011). BIM- and 4D-based integrated solution of analysis and management for conflicts and
structural safety problems during construction: 1. Principles and methodologies. Automation in Construction, 20(2),
167–180. https://doi.org/10.1016/j.autcon.2010.09.014
Zhang, L., Wu, X., Ding, L., Skibniewski, M. J., & Lu, Y. (2016). Bim-Based Risk Identification System in tunnel construction.
Journal of Civil Engineering and Management, 22(4), 529–539. https://doi.org/10.3846/13923730.2015.1023348
Zhang, S., Boukamp, F., & Teizer, J. (2015). Ontology-based semantic modeling of construction safety knowledge: Towards
automated safety planning for job hazard analysis (JHA). Automation in Construction, 52, 29–41.
https://doi.org/10.1016/j.autcon.2015.02.005
Zhang, S., Sulankivi, K., Kiviniemi, M., Romo, I., Eastman, C. M., & Teizer, J. (2015). BIM-based fall hazard identification and
prevention in construction safety planning. Safety Science, 72, 31–45. https://doi.org/10.1016/j.ssci.2014.08.001
Zhang, S., Teizer, J., Lee, J. K., Eastman, C. M., & Venugopal, M. (2013). Building Information Modeling (BIM) and Safety:
Automatic Safety Checking of Construction Models and Schedules. Automation in Construction, 29, 183–195.
https://doi.org/10.1016/j.autcon.2012.05.006
Zhang, S., Teizer, J., Pradhananga, N., & Eastman, C. M. (2015). Workforce location tracking to model, visualize and analyze
workspace requirements in building information models for construction safety planning. Automation in Construction,
60, 74–86. https://doi.org/10.1016/j.autcon.2015.09.009
Zhang, Y., Xing, X., & Antwi-Afari, M. F. (2021). Semantic IFC data model for automatic safety risk identification in deep
excavation projects. Applied Sciences (Switzerland), 11(21). https://doi.org/10.3390/app11219958
Zhou, C., Luo, H., Fang, W., Wei, R., & Ding, L. (2019). Cyber-physical-system-based safety monitoring for blind hoisting with
the internet of things: A case study. Automation in Construction, 97, 138–150.
https://doi.org/10.1016/j.autcon.2018.10.017
Zhou, M., Tang, Y., Jin, H., Zhang, B., & Sang, X. (2021). A bim-based identification and classification method of environmental
risks in the design of beijing subway. Journal of Civil Engineering and Management, 27(7), 500–514.
https://doi.org/10.3846/jcem.2021.15602
Zou, Y., Kiviniemi, A., & Jones, S. W. (2016). Developing a tailored RBS linking to BIM for risk management of bridge projects.
Engineering, Construction and Architectural Management, 23(6), 727–750. https://doi.org/10.1108/ECAM-01-2016-
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0009
This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=4595455
Preprint not peer reviewed