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This research reviews recent advances in the domain of Automated Rule Checking (ARC) and argues that current systems are predominantly designed to validate models in post-design stages, useful for applications such as e-permitting. However, such a design-check-separated paradigm imposes a burden on designers as they need to iteratively fix the fail...
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... decomposer module is needed to break down an IFC file into floors and spaces, or a washroom module is needed to identify which spaces are used for washrooms, whether the washroom is for men, women, or other, and how many water closets/urinals are in the washroom. Figure 4 displays the modularized architecture of the proposed product by the developers. As demonstrated, the user interacts with the UI and provides preliminary information required for checking processes. ...
Citations
... Level 3A is often achieved by project enterprises, general contractors, or consortia of companies. Nevertheless, despite various ontology-level solutions, designers are still often forced to iteratively fix interoperability failures [19]. Level 3B is a similar challenge. ...
The integration of the Internet of Things (IoT) and Building Information Modeling (BIM) is progressing. The use of microcontrollers and sensors in buildings is described as a level 3B maturity in the use of BIM. Design companies, contractors and building operators can use IoT solutions to monitor, analyze or manage processes. As a rule, solutions based on original Arduino boards are quite an expensive investment. The aim of this research was to find a low-cost IoT solution for monitoring , analysis and management, and integrate it with a BIM model. In the present study, an inexpensive NodeMCU microcontroller and a temperature and pressure sensor were used to study the thermal comfort of users in a single-family home. During the summer season, analysis of the monitored temperature can contribute to installation (HVAC) or retrofit work (for energy efficiency). The article presents a low-cost solution for studying the thermal comfort of users using a digital twin built-in BIM. Data obtained from sensors can support both the design and management processes. The main contribution of the article enables the design, construction and use of low-cost circuits (15.57 USD) even in small developments (single-family houses, semi-detached houses, terraced houses, atrium buildings). Combining IoT sensor telemetry with BIM (maturity level 3C) is a challenge that organizations will face in the near future.
... However, the existing codes are still written in natural language and mainly read and utilized by domain experts; thus, these codes are difficult for computers to automatically understand, process, and analyze (Ismail et al., 2017). Therefore, automated rule interpretation methods that aim to automatically interpret regulatory texts into a computer-processable format have been studied by many researchers for automated compliance checking and even intelligent design (Eastman et al., 2009;Ismail et al., 2017;Sobhkhiz et al., 2021;Fuchs, 2021). ...
Interpreting regulatory documents or building codes into computer-processable formats is essential for the intelligent design and construction of buildings and infrastructures. Although automated rule interpretation (ARI) methods have been investigated for years, most of them are highly dependent on the early and manual filtering of interpretable clauses from a building code. While few of them considered machine interpretability, which represents the potential to be transformed into a computer-processable format, from both clause-and document-level. Therefore, this research aims to propose a novel approach to automatically evaluate and enhance the machine interpretability of single clauses and building codes. First, a few categories are introduced to classify each clause in a building code considering the requirements for rule interpretation, and a dataset is developed for model training. Then, an efficient text classification model is developed based on a pretrained domain-specific language model and transfer learning techniques. Finally, a quantitative evaluation method is proposed to assess the overall interpretability of building codes. Experiments show that the proposed text classification algorithm outperforms the existing CNN-or RNN-based methods, by improving the F1-score from 72.16% to 93.60%. It is also illustrated that the proposed classification method can enhance downstream ARI methods with an improvement of 4%. Furthermore, analysis of more than 150 building codes in China showed that their average interpretability is only 34.40%, which implies that it is still difficult to fully transform an entire regulatory documents into computer-processable formats. It is also argued that the interpretability of building codes should be further improved both from the human side (considering certain constraints when writing building codes) and the machine side (developing more powerful algorithms, tools, etc.)..
... A series of studies have highlighted the various challenges of further using BMC in the building industry (Beach et al., 2020;Sobhkhiz et al., 2021). Beach et al. (2020) made a survey that indicated those various aspects of socio-technical challenges that were experienced to hinder further use. ...
... This highlights a discrepancy between what has previously been reported (Beach et al., 2020;Peter Nørkjaer Gade et al., 2018;Peter Nørkjaer Gade et al., 2021;Sobhkhiz et al., 2021) about the main challenges for a better adoption of BMC systems in the industry, namely, the socio-technical problems. These findings could indicate that to better deal with the socio-technical problems of adopting BMC systems, the research community could benefit of using more empirical data from practices in order to understand why some characteristics in the suggested BMC artefacts are unwanted and some are welcomed. ...
... Broadly speaking, intelligent construction can be divided into three main groups. Group 1, based on the BIM and IoT technologies, intelligent construction achieves information interaction between people and things, and things and things to promote the efficiency of the construction process Zhang et al., 2021); Group 2, base on the AI technology, the processes of design (Sobhkhiz et al., 2021), construction (Wang et al., 2016), and management (Lin & Wu, 2021) can be optimized. And group 3, the collection, analysis, and management of big data generated during the construction process can be conducted through cloud computing and big data technology (Li et al., 2016;Meng, 2015). ...
With the development of information technologies and the transformation and upgrading of architecture, engineering, and construction (AEC) industry, intelligent construction is gaining increasing attention. Integrating with construction industrialization, intelligent construction has become an important way to achieve high-quality development of the AEC industry. To grasp the research development status of intelligent construction and explore future research opportunities, this study takes the database of Web of Science (WOS) and China National Knowledge Infrastructure (CNKI) as the data source and carries out a bibliometric analysis of related research based on VOSviewer and Gephi. For English articles, the studies earlier focused on theoretical methods and gradually transitioned to the engineering practice. Although it starts relatively later according to Chinese articles, intelligent construction has a rapid development in recent years and shows close interactions with engineering practice. Finally, this paper puts forward research prospects, in order to provide some directions for the research of intelligent construction and contribute to the upgrading and transformation of the AEC industry.
... Numerous studies concluded positive impacts of implementing Automated Rule Checking (ARC) from project cost reduction (Beach et al., 2015, Eastman et al., 2009, Greenwood et al., 2010, design productivity (Hjelseth, 2009, Dimyadi andAmor, 2013), to communication between Architectural, Engineering and Construction (AEC) experts (Sobhkhiz et al., 2021). ARC in this domain refers to checking the compliance of Building Information Modeling (BIM) models to a set of predefined, computer-based rules; it is typically applied in regulation compliance checking (El-Diraby, 2019). ...
... Despite the benefits, it is reported that ARC falls short of commercial use in relation to the maturity of the technology in the research area (Revfik et al., 2014, Amor andDimyadi, 2021). The findings of several experts in this field suggest that socio-technical challenges are the main barriers to implementation (Gade and Svidt, 2021, Lucas and Vijayarao, 2019, Sobhkhiz et al., 2021, that are present both on legislative, organizational, and individual levels. Gade and Svidt (2021) further argues that research around BMC faces a dearth of social science perspectives, and that understanding human behavior would shed light to the depths of these already identified issues. ...
Automated solutions to building compliance checking has been slow in its implementation in the Danish construction industry. Mainly socio-technical barriers, such as trust in automation challenge the spread of technological advances from research and development. This paper studies the trust relationship of Architectural, Engineering, and Construction (AEC) professionals towards Automated Rule Checking (ARC) systems in Denmark. The 3S-model from the field of cognitive psychology was employed to help understanding the varying ways people assess information credibility. Three hypotheses were tested through experimentation to investigate the significance of different information features, and pertaining user characteristics on human-automation trust behaviors in the domain of ARC. Although the highly specified research area targeted a relatively small demographic within the Danish construction industry, the findings present an interesting new perspective on the common characteristics and trust behaviors of the end user. With the refinement of the framework’s application, the 3S-model can elucidate the many factors that are believed to influence technology acceptance within the industry. This research aims to contribute to the development of ARC solutions with a front-end viewpoint on trustworthiness.
... A series of studies have highlighted the various challenges of further using BMC in the building industry (Beach et al., 2020;Sobhkhiz et al., 2021). Beach et al. (2020) made a survey that indicated those various aspects of socio-technical challenges that were experienced to hinder further use. ...
... This highlights a discrepancy between what has previously been reported (Beach et al., 2020;Peter Nørkjaer Gade et al., 2018;Peter Nørkjaer Gade et al., 2021;Sobhkhiz et al., 2021) about the main challenges for a better adoption of BMC systems in the industry, namely, the socio-technical problems. These findings could indicate that to better deal with the socio-technical problems of adopting BMC systems, the research community could benefit of using more empirical data from practices in order to understand why some characteristics in the suggested BMC artefacts are unwanted and some are welcomed. ...
In both research and industrial reports, one of the major challenges for Building Information Modelling and checking that remains is of a socio-cultural nature, for example, with cultural resistance. While this has remained one of the main challenges for years, this article aims to investigate how contemporary model checking research approaches this challenge. Therefore, the article contains a structured literature review to map out methodical approaches and the use of empirical data from the industry practices to highlight how the research domain interacts with the problems situated in the practices. The study presents a review of 71 papers
that shows the limited use of data from practice and a clear methodical trend of using artefact development with limited evaluation of empirical data from practice. These findings indicate a gap in the research domain of representing practice in model checking research.
... The proposed method is applicable for creating computable rules from various textual regulatory documents. For example, in proactive design, a designer can use the proposed method to create computable rules from specific or customized textual regulatory documents to check the models [52]. The proposed automated rule interpretation method can cover most cases. ...
As an essential prodecure to improve design quality in the construction industry, automated rule checking (ARC) requires intelligent rule interpretation from regulatory texts and precise alignment of concepts from different sources. However, there still exists semantic gaps between design models and regulatory texts, hindering the exploitation of ARC. Thus, a knowledge-informed framework for improved ARC is proposed based on natural language processing. Within the framework, an ontology is first established to represent domain knowledge, including concepts, synonyms, relationships, constraints, etc. Then, semantic alignment and conflict resolution are introduced to enhance the rule interpretation process based on predefined domain knowledge and unsu-pervised learning techniques. Finally, an algorithm is developed to identify the proper SPARQL function for each rule, and then to generate SPARQL-based queries for model checking purposes, thereby making it possible to interpret complex rules where extra implicit data needs to be inferred. Experiments show that the proposed framework and methods successfully filled the semantic gaps between design models and regulatory texts with domain knowledge, which achieves a 90.1% accuracy and substantially outperforms the commonly used keyword matching method. In addition, the proposed rule interpretation method proves to be 5 times faster than the manual interpretation by domain experts. This research contributes to the body of knowledge of a novel framework and the corresponding methods to enhance automated rule checking with domain knowledge.
... Andrich et al. [9] present a checking flow of BIM models in order to identify the subjects responsible for each specific check during the process in a detailed design phase. Sobhkhiz et al. [10] propose an IFC-based Automated Rule Checking (ARC) system to validate models, aiming to achieve proactive bottom-up solutions building upon the requirements and resources of end-users in post design phases. ...
We are very happy that the Special Issue “Advanced BIM Application in Construction and Buildings” of Buildings has been published [...]
... Since the system is still in the research team's internal usage stage, it has not been extensively utilized, so we do not have enough data to conduct a study on how user-friendly the system is and how long the investment return time is. By collaborating closely with end-users, proactive systems could be developed [36]. Ideally, we would conduct surveys to determine the effectiveness and usability of the current system, and a focus group that uses the system and reports feedback would be required to help evaluate the system. ...
Due to the growing complexity of mechanical, electrical, and plumbing (MEP) designs and the rules that govern them, performing rule checks manually has become expensive. However, MEP-based rule checking has not received adequate attention compared to automated rule checking in other domains. Based on Knowledge Management and Building Information Modeling (BIM), an automated rule checking system integrated knowledge base management system (KBMS) for model information expansion, information extraction, system integrity checking, and element spacing checking was developed. MEP rules for automated rule checking were collected, optimized, and stored in the MEP knowledge base. The KBMS facilitates the management of MEP rules in the knowledge base. A Revit plug-in of MEP rule checking system was developed including functions of KBMS, Model Integrity Checking, Elements Space Checking, and Locating the non-compliant element in model view. This study integrated both KBMS and BIM technologies to achieve automated rule checking for MEP. This simplifies the process of rule checking of MEP systems in an automated manner.
Industry Foundation Classes (IFCs), as the most recognized data schema for Building Information Modeling (BIM), are increasingly combined with ontology to facilitate data interoperability across the whole lifecycle in the Architecture, Engineering, Construction, and Facility Management (AEC/FM). This paper conducts a bibliometric analysis of 122 papers from the perspective of data, model, and application to summarize the modes of IFC and ontology integration (IFCOI). This paper first analyzes the data and models of the integration from IFC data formats and ontology development models to the IfcOWL data model. Next, the application status is summed up from objective and phase dimensions, and four frequent applications with maturity are identified. Based on the aforementioned multi-dimensional analysis, three integration modes are summarized, taking into account various data interoperability requirements. Accordingly, ontology behaves as the representation of domain knowledge, an enrichment tool for IFC model semantics, and a linkage between IFC data and other heterogeneous data. Finally, this paper points out the challenges and opportunities for IFCOI in the data, domain ontology, and integration process and proposes a building lifecycle management model based on IFCOI.
