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BIM AND GIS INTEGRATION FOR INFRASTRUCTURE ASSET MANAGEMENT:
A BIBLIOMETRIC ANALYSIS
M. Garramone1*, N. Moretti1, M. Scaioni1, C. Ellul2, F. Re Cecconi1, M. C. Dejaco1
1 Dept. of Architecture, Built Environment and Construction Engineering, Politecnico di Milano, via Ponzio 31, 20133 Milano, Italy
– (manuel.garramone, nicola.moretti, marco.scaioni, fulvio.rececconi, mario.dejaco)@polimi.it
2 Dept. of Civil, Environmental and Geomatic Engineering, University College London, Gower Street, London, WC1E 6BT UK -
c.ellul@ucl.ac.uk
KEY WORDS: BIM, GIS, Asset Management, digitisation, information management, infrastructure
ABSTRACT:
The integration of Building Information Modelling (BIM) and Geographical Information Systems (GIS) is gaining momentum in
digital built Asset Management (AM), and has the potential to improve information management operations and provide advantages
in process control and delivery of quality AM services, along with underlying data management benefits through entire life cycle of
an asset. Work has been carried out relating GeoBIM/AM to buildings as well as infrastructure assets, where the potential financial
savings are extensive. While information form BIM maybe be sufficient for building-AM; for infrastructure AM a combination of GIS
and BIM is required. Scientific literature relating to this topic has been growing in recent years and has now reached a point where a
systematic analysis of current and potential uses of GeoBIM in AM for Infrastructure is possible. Three specific areas form part of the
analysis – a review of BIM and Infrastructure AM and GIS and Infrastructure AM leads to a better understanding of current practice.
Combining the two, a review of GeoBIM and Infrastructure AM allows the benefits of, and issues relating to, GeoBIM to be clearly
identified, both at technical and operational levels. A set of 54 journal articles was selected for in-depth contents analysis according to
the AM function addressed and the managed asset class. The analysis enabled the identification of three categories of issues and
opportunities: data management, interoperability and integration and AM process and service management. The identified knowledge
gaps, in turn, underpin problem definition for the next phases of research into GeoBIM for infrastructure AM.
1. INTRODUCTION
Asset Management (AM) is a primary organisational function for
realising value from assets, balancing risk, costs and
opportunities (ISO, 2014). This discipline is not new and in the
last 30 years, it has been defined, standardised and adapted to
different fields, providing support for the operation and
management of assets and ensuring that improved asset
performance and higher quality decision making helps to achieve
business objectives (Amadi-Echendu et al., 2010). AM is the
function that connects the core business of an organisation to the
infrastructure (digital and physical) that must be operated for its
success.
Despite underpinning the life cycle of a physical entity in terms
of technical, financial and user-oriented performance, AM is a
relatively late adopter of the digital innovation that could be
exploited to better face the current challenges in the Architecture,
Engineering, Constructions and Operations (AECO) sector.
However, in recent years management of the Built Environment
(BE) is undergoing a revolution due to the digital transformation
that is affecting the management tools, processes and the
definition of the asset itself (Parn and Edwards, 2019).
Additionally, the physical asset is increasingly included in the set
of information, processes and software platform that are
employed from the design to the use/operational phase of its life
cycle: the digital environment, resulting in integrated
digital/physical systems. This dynamic can be defined as the
digitisation of management of the built environment and is
shaping a new paradigm in AECO (Saxon et al., 2018). The
integrated digital/physical asset is characterised both by the
performance related to the functioning of the physical elements
(e.g. the comfort performance of an indoor space of a facility, the
* Corresponding author
load capacity of a bridge or the water flow rate of a section in a
water supply system etc.) and by the services that can be
delivered through its digital replica.
Data relating to the asset and the surrounding built environment
can be sourced from a wide range of disparate sources – sensors,
3D models, engineering drawings, maintenance reports and
schedules, financial reports, time-tables, personnel details and
more. Location – where asset data is situated to in 3D space –
provides perhaps the most important approach to integrating
(linking) this new digital data, allowing, for example, sensor data
for room temperature to be examined in conjunction with
occupancy data for the room. Two location-enabled tools are at
the forefront of the management of the new complexity of the
digital BE: Building Information Modelling (BIM) and
Geographic Information Systems (GIS).
The British Standards Institute (BSI) defines BIM as “the
management of information flows along the life cycle of the asset
through the use of digital modelling” (BSI, 2018). Therefore,
adopting a BIM approach means implementing a set of digital
processes, empowered by digital tools, procedures,
methodologies, furthering efficiency of the information exchange
and collaboration among parties. GIS are defined as a "computer-
based information system that enables capture, modelling,
storage, retrieval, sharing, manipulation, analysis, and
presentation of geographically referenced data'' (Worboys and
Duckham, 2004). Very broadly, GIS can provide high-level
information about the context of an asset and about the asset itself
and its operation, covering an extended geographical area, BIM
focuses more on structural and engineering detail for specific
projects (Ellul, 2018). From the AM perspective, the potential in
the employment of these two approaches can be found in the
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume VI-4/W1-2020, 2020
3rd BIM/GIS Integration Workshop and 15th 3D GeoInfo Conference, 7–11 September 2020, London, UK
This contribution has been peer-reviewed. The double-blind peer-review was conducted on the basis of the full paper.
https://doi.org/10.5194/isprs-annals-VI-4-W1-2020-77-2020 | © Authors 2020. CC BY 4.0 License.
77
information and process management capabilities offered,
enabling the improvement not only in the design and construction
phase, but also of operations in the use phase (Dixit et al., 2019).
Linking the data provided by GIS and BIM can underpin the
development of an integrated digital model of the built asset,
supporting advanced information management in the digital built
environment.
This integration is broadly defined as “GeoBIM” and overcoming
process and information management issues across different
stages of the assets’ life cycle (Ellul et al., 2018). GeoBIM as a
topic has been subject of study at the international level in recent
years (e.g. Noardo et al., 2019, Wang et al. 2019). The
implementation of the GIS/BIM integrated approaches to address
multidisciplinary problems in AM is, therefore, gaining
momentum. Additionally, while BIM on its own – with its focus
on detail of, in particular, indoor environments – could
potentially provide information for AM of buildings, GeoBIM
integration is particularly important for infrastructure – where an
asset could be located over a large area, at mapping scales more
suited to GIS, with engineering detail from BIM.
While meta-studies have been carried out reviewing technical
approaches to BIM and GIS integration (e.g. (Wang, 2019) a
systematic analysis of the scientific literature in GIS/BIM
integration for AM is missing, in particular in the context of
infrastructure, and with AM rather than GIS or BIM its main
focus. This article provides a systematic review of the
bibliography, collected from Web of Science (WoS) and Scopus,
identifying trends, issues and opportunities relating to the use of
GeoBIM for infrastructure AM. This, in turn, yields a better
understanding of the knowledge gaps to be addressed in a
GeoBIM for infrastructure AM research agenda.
2. METHODS
The research was conducted in early April 2020 following the
standard process of systematic literature review (Moher et al.,
2015): identification, screening, eligibility and inclusion. The
first step concerns the definition of the research keywords to
select a set of articles corresponding to the boundaries of the
research field in BIM, GIS and the integration of the two for
infrastructure AM. The Scopus and WoS databases have been
queried with keywords represented in Figure 1. The use of wild
characters and the boolean operators (“AND” and “OR”) resulted
in 226 results from Scopus and 219 from WoS1.
Figure 1. Search terms used in Scopus (“Article title, Abstract,
Keywords” field) and in WoS (“Topic” field).
The results have then been further filtered to remove references
not relevant for the scope of the research (filtering by subject
area), and by category, again excluding those not related to the
scope of the research (e.g. medicine, art, chemistry, etc.). This
step resulted in the identification of 181 (Scopus) and 179 (WoS)
references. Once the sample was defined, bibliometric analyses
were carried out and historical data trends and network analysis
has been obtained, using the R package Bibliometrix, (Aria and
Cuccurullo, 2017), version 2.3.2 dated 23/11/2019. The sample
1 As noted above, this search is specifically AM focussed – i.e.
papers relating to IFC and CityGML interoperability or to
other GeoBIM applications are excluded.
was then reduced to a number of papers appropriate for contents’
analysis. To achieve this, references have been:
1. filtered by year, considering the references published in
between 2013 and 2020, a period in which a clear
increasing in the literature production can be identified
(after 2013 more than 10 articles per year have been
published in the Scopus database). Subtotal results
obtained are 108 (Scopus) and 121 (WoS);
2. refined by document type. In order to reduce the
complexity of the databases, in this research only the
journal articles in English were considered. Subtotal results
obtained are 40 (Scopus) and 71 (WoS);
3. refined according to an in-depth review of title and abstract,
selecting only the documents with high research relevance.
Subtotal results obtained are 31 (Scopus) and 46 (WoS);
4. refined by removing duplicates and merging the datasets
obtained from the two databases.
The final set of 54 articles was then categorised according to the
type of approach and technology adopted in AM: BIM, GIS, the
integration of the two and the AM function addressed. A similar
classification has been carried out considering the asset class
managed. These meta and content analyses enabled the
identification of the knowledge gaps to be addressed in a research
agenda in GeoBIM for infrastructure AM.
3. RESULTS
3.1. Bibliometric analysis
Considering the annual scientific production represented in
Figure 2 an increasing interest in the use of BIM and GIS for
infrastructure AM is evident in recent years. The annual growth
rate, excluding the year 2020 (not yet completed at the time of
the research) is 12,99% (Scopus) and 9,05% (Web of Science).
Figure 2. Published scientific literature (1991-2020)
Since the first published article in Scopus, a first peak can be seen
around 2006, when the annual summary of the scientific
production in both databases exceeded 15 articles. However, the
first significant increase is registered after 2004, when the sum of
the publications in both databases started to be almost always
around 10 documents, with a peak in 2006. After 2013 an even
more relevant increase is shown.
In order to highlight how the research field is structured, some
network analyses have also been carried out. Authors’ keywords
were considered with the number of nodes of 25 and a minimum
edge of 2. Figure 3 shows the most influential terms. The larger
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume VI-4/W1-2020, 2020
3rd BIM/GIS Integration Workshop and 15th 3D GeoInfo Conference, 7–11 September 2020, London, UK
This contribution has been peer-reviewed. The double-blind peer-review was conducted on the basis of the full paper.
https://doi.org/10.5194/isprs-annals-VI-4-W1-2020-77-2020 | © Authors 2020. CC BY 4.0 License.
78
size of the circle represents the greater occurrence and the links
describe the strength of the co-occurrence of terms. The centrality
of AM and the big dimensions of BIM and GIS clusters are to be
expected as they are the terms used to build up the query.
Examining the links, is clear that AM is supported by GIS or BIM
tools, but as yet a strong link between the two tools cannot be
identified, indicating a gap to be further investigated.
Figure 3. Keywords co-occurrence network (Scopus database)
Moreover, Figure 3 highlights five clusters of keywords
primarily indicating the link between the digital tools and the AM
functions, suggesting the link between GIS, condition assessment
and decision making (red cluster); sensors and condition
monitoring (blue cluster); remote sensing and GIS (green
cluster), data management and big data (orange cluster); BIM and
Facility Management (FM) (purple cluster). Figure 4 shows the
same analysis for WoS. The clusters are similar, although more
importance (dimension of the circle) given to BIM compared to
GIS and, again a link appears between the AM functions and the
digital tools, especially concerning the purple cluster indicating a
relationship among BIM, big data, FM and the blue cluster,
connecting the GIS domain to infrastructure AM.
Figure 4. Keywords co-occurrence network (WoS database)
Social structure (Figure 5) was analysed through the countries’
collaboration network with a number of nodes of 20 and a
minimum edge of 1. As shown in Figure 5, the social structure in
Scopus is slightly different from the one obtained from WoS. In
fact, Scopus presents four clusters, corresponding
approximatively to four main geographic areas, led by USA, UK
and Australia. A situation confirmed by the same analysis carried
out on Scopus references, though to the most productive
countries already identified in Scopus database, Italy and China
can be added. Moreover, in this case, the five clusters do not
correspond to any homogeneous geographic areas.
Figure 5. Collaboration Network, by Countries (Scopus at the
top and WoS at the bottom)
In order to allow comparison, the analysis described above has
been carried out on the whole dataset (181 references in Scopus
and 179 in WoS). The following results related to the 54 journal
articles obtained after the further filtering steps described in
Section 2 (steps 1 to 4). Figure 6 represents the results of the
filtering before merging the two datasets, highlighting a higher
presence of articles in the WoS database, and after merging them.
In line with the keywords co-occurrence network, most of the
articles are developed using or BIM or GIS with a much smaller
percentage relating to the use of both the tools. The final selection
consists of 16 articles BIM-oriented, 29 articles GIS-oriented and
9 articles with BIM-GIS integration and Infrastructure Asset
Management, for total.
3.2. BIM for Infrastructure Asset Management:
The first set of references considered concerns the use of the BIM
approach for infrastructure AM. To date, the BIM approach is
mostly employed during the design and construction phases,
rather than in operations and facilities management (Hassan
Ibrahim, 2013) despite representing an added value for the
information management during the latter (Bosch et al. 2015,
Parlikad and Catton 2018).
3.2.1 Information management and uncertainty: An accurate
BIM model can support operation and maintenance through the
integration of the existing AM system and the model data
(Heaton et al., 2019). Additionally, it can provide great
advantages in the re-baselining process during AM operations,
through a periodic four-step workflow based on collecting,
verifying, processing and updating of asset data (Abdirad and
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume VI-4/W1-2020, 2020
3rd BIM/GIS Integration Workshop and 15th 3D GeoInfo Conference, 7–11 September 2020, London, UK
This contribution has been peer-reviewed. The double-blind peer-review was conducted on the basis of the full paper.
https://doi.org/10.5194/isprs-annals-VI-4-W1-2020-77-2020 | © Authors 2020. CC BY 4.0 License.
79
Dossick, 2020). In fact, according to Love et al. (2015) the
development of a BIM model for asset management should start
at the outset of the project, considering the whole asset life-cycle
in order to facilitate the information exchange and across the
several stages of the management process, supporting strategic
decision making and reducing uncertainty in asset management
(Krystallis et al., 2016). Moreover, Love et al. (2017b) further
confirm this position, analysing the cost performance of 16 rail
projects, demonstrating that the implementation of BIM can
improve cost certainty during the construction process. The
implementation of BIM for infrastructure AM also allows better
data integration: combining various temporal data categories for
two bridges, Zhang et al. (2018) proposed a 4D-based model for
supporting predictions and driving maintenance activities.
Figure 6. Selected articles concerning different information
management approaches in WoS, Scopus and both
databases.
3.2.1. Process integration: Process management is a crucial
issue in BIM implementation for infrastructure AM. In rail
projects, for instance, the digital models are large and complex
and should be accompanied by advanced AM processes, able to
leverage the potential of the digital models and of the related non-
graphical information (Dell’Acqua et al., 2018). Moreover, using
BIM applications during planning and delivery phases of rapid
transit projects enables a more efficient operation and
maintenance through integration with asset-monitoring systems
(Saldanha, 2019).
3.2.2. Asset performance monitoring
AM cannot exist without an effective monitoring and control
system. Therefore, Delgado et al. (2017) propose a BIM-based
approach for structural performance monitoring in order to
visualize, manage, interpret and analyse data collected by
structural health monitoring systems
3.2.3. Other approaches: Alternatives to BIM include the
development of a relationally integrated value network
(RIVANS) for total asset management (TAM) (Smyth et al.,
2017). Additionally, some authors developed frameworks and
workflows considering infrastructure case studies (electrical
systems, rail transport and bridges). For instance, Love et al.
(2016) starting from the issues in omissions in as-built CAD
documentation happening in traditional design and management
approaches and from the potential of the BIM tools, proposed a
System Information Model (SIM) for digital asset management
of electrical infrastructures.
3.3. GIS for Infrastructure Asset Management
As shown in Figure 6, the majority of the results obtained relate
to the use of GIS for infrastructure AM. This trend could be
explained by two reasons: firstly, GIS was developed in the ‘60s,
and thus it has been evolving from a specialist technology to an
interdisciplinary tool for more than 50 years (Bishop and
Grubesic, 2016); secondly, GIS is used for spatial data and
analysis over large geographical extends, and to assess
infrastructure it is necessary to visualize how they relate to their
surrounding environment (Zhao et al., 2019).
3.3.1. Risk management: Risk assessment, and disaster
planning and mitigation is a major topic in both transportation
and utility infrastructure AM. Different GIS approaches are used
to assess transportation system vulnerabilities (Kim et al., 2013),
densely populated urban areas (Sherly et al., 2015) and the
interactions between different infrastructure networks in order to
develop an integrated assessment of service vulnerabilities
(Inanloo et al., 2016). Climate change and urbanization have led
to the development of frameworks for disaster prevention
(Nakamura et al., 2019), frequency and severity (Kruel, 2016).
For instance, Espada et al., (2015) proposed a spatial framework
for critical infrastructure systems focused on climate adaptation
and flood mitigation. Through the construction of thematic maps
of vulnerability and damage (Scaini et al., 2014), it is also
possible to develop rapid maps of disaster events (Ajmar et al.,
2017).
3.3.2. Asset performance monitoring: An important aspect
of AM is performance management (control, monitoring and
optimization) through the whole asset life-cycle. The
construction of a set of indicators, focused on the transportation
system, can support the decision-making process (Chatziioannou
and Álvarez-Icaza, 2017). Torres-Machi et al. (2018) proposed a
GIS platform for the integration of technical, economic,
environmental, social and political aspects in the life cycle
assessment of a network to support the management of
transportation assets. Zhang et al. (2013) developed a model to
collect, manage and visualize pavement condition data; while, Li
et al. (2018) presented a network to integrate quantitative
condition data relating to crosswalks and intersections, to better
manage maintenance. Different workflows and models are used
to assess utility infrastructure in order to simulate deterioration
and failure (Ward et al., 2017), the remaining lifecycle and the
network robustness (Goyal et al., 2016).
3.3.3. Asset cost control: Cost planning and control has a
fundamental role in AM. Ward et al. (2014) consider the link
between investment cost and asset life cycle to evaluate potential
serviceability improvements, through rehabilitation model in
which GIS tools are used to identify the geospatial nature of
serviceability incidents. Feliciano et al. (2014) analyse the
investment payback period, Net Present Value (NPV) and energy
production when valuing intervention alternatives with a
hydraulic model based on GIS data.
3.3.4. Other Approaches: A number of authors propose
different tools for infrastructure AM. Pfeiffer et al. (2017)
developed a Grassroots Infrastructure Dependency Model
(GRID-M) to enable near-real-time analysis of physical
infrastructure dependencies of specific supply and demand
nodes; while, Kuller et al. (2019) presented a Spatial Suitability
Analysis Tool (SSANTO) to map, through “needs” and
“opportunities”, suitability for Water Sensitive Urban Design
(WSUD) assets.
Scopus Web Of Science
Both databases
BIM
GIS
BIM&GIS
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume VI-4/W1-2020, 2020
3rd BIM/GIS Integration Workshop and 15th 3D GeoInfo Conference, 7–11 September 2020, London, UK
This contribution has been peer-reviewed. The double-blind peer-review was conducted on the basis of the full paper.
https://doi.org/10.5194/isprs-annals-VI-4-W1-2020-77-2020 | © Authors 2020. CC BY 4.0 License.
80
3.4. Integration of BIM and GIS
The smallest number of papers (16.6%) relates to the integration
of BIM and GIS for AM. The integration of building data and
geographic information is an important interoperability challenge
(Ellul, 2018) and results in access to information at different
scales relating to the asset and its wider context. A number of
authors focused on technical problems while others analysed case
studies.
3.4.1. Information Management and interoperability:
Highlighting the lack of attention paid to information
management over the built asset life-cycle, Hoeber and Alsem
(2016) present a concept for a way of working based on the
collaboration between asset managers and project managers from
the start of the project and during the life-cycle of the assets, with
the use of exchange standards. Guillen et al. (2016) underlined
the importance of an environment of software interoperability of
asset management, with the BIM model linked to other data
sources, such as GIS, Building Automation Systems (BAS) and
FM Systems (FMS). The conversion from BIM standard (IFC) to
GIS standard (CityGML) and vice versa is not always accurate,
and for this reason, a number of authors used a third-party
platform to manage different data sources. Zadeh et al. (2019)
presented a BIM-CityGML data integration (BCDI) approach,
based on a mediated schema, with the aim to collect and manage
data that can be queried simultaneously from both sources.
3.4.2. Integration and decision making: Considering
transportation infrastructure, Aziz et al. (2017) proposed the use
of an open-source cloud computing platform, Hadoop, to allow
for continuous flow of data throughout an asset’s life cycle;
while, Sankaran et al. (2018) considered the potential benefit of
using Civil Integrated Management (CIM), a set of practices and
tools that can facilitate the workflow of highway project delivery
and management. Other authors presented frameworks for utility
infrastructure management: Edmondson et al. (2018) developed
a prototype to aid prediction and decision making about a
sewerage network; Lee et al. (2018) presented a framework,
based on the integration of BIM/GIS, to improve performance of
current maintenance management system; Love et al. (2018)
developed a System Information Model (SIM), in which each
component has geometric data (3D model), non-geometric data
(type and functionality) and geographic data. The use of the SIM
has different benefits (Love and Matthews, 2019), such as cost-
saving, improvement in information quality and in asset integrity,
but it’s effective if data are integrated during each phase of the
life-cycle asset, from the design to the operation.
3.5. Classification by AM Function
The final stage of analysis relates to the analysis of the merged
sample according to the core AM function and to the main asset
class. Table 1 shows the results of the classification by AM
function, namely the main processes implemented by an AM
organisation for supporting its business. 14 core AM functions
have been identified, organised according to their decision-
making level (using an analytical approach based on that
described in Re Cecconi et al. (2020), not described here for
synthesis reasons). In Table 1, articles have been categorised by
the type of approach addressed by the authors (BIM, GIS and
BIM/GIS) and the asset class considered (Roads, Water supply
networks, Electrical Energy networks etc.). This allows the
identification of where the three approaches, and especially the
implementation of the BIM/GIS integration, are mostly used.
Figure 7. Information management approaches and asset
classes. Each paper was counted once, and the
papers focused on multiple case studies were
included in the “Multiple Infrastructure” category.
4. DISCUSSION
The bibliometric analyses described in this paper identified the
main characteristics of the literature in BIM and GIS and the
integration of the two for infrastructure AM. Literature produced
in the selected period shows a greater focus on GIS, especially
for the strategic and tactical AM functions.
Figure 7 shows the paper count by main asset class and highlights
the versatility of the GIS approach in infrastructure AM, which
is also reflected by having the highest percentage of articles
(53.7%) in this group – maybe due to the long research history in
GIS and the ability of this tool to manage very diverse types of
data. Roads appear to be the asset class mostly addressed by case
studies, along with the management of multiple infrastructures.
Within the papers reviewed, a greater interest in BIM relates to
AM function belonging to the tactical and operational level of the
decision-making process (Facility, Project and Data
Management). Also, Table 1 shows that the BIM approach has
been mainly applied to the management of Bridges and Rail
Transport networks, although the majority of the references do
not address any specific asset class. The literature production, in
this case, is smaller and accounts for the 29.6% of the total.
Articles on GIS/BIM integration are even fewer, representing
16.6% of the total. In this case, the most addressed asset classes
are roads and electrical energy networks. Moreover, looking at
Table 1, BIM/GIS integration research appears to be mainly
focussed in the Facility and Data Management functions, again
indicating a tactical/operational characterisation of this approach.
However, the literature on this topic is still very recent and does
not illustrate the whole potential of the BIM/GIS integrated
approach for infrastructure AM.
Altogether, glancing at the asset classes addressed by the three
groups of articles studied it can be stated that linear transport
infrastructures (roads and railways) are the asset classes where
BIM, GIS and integrated approaches have been implemented and
tested the most. GIS approaches have the longest story in AM
and this may lead to a wider literature on case studies
implementation. However, the enhanced versatility of the
GIS/BIM integrated approach may lead in the next years to the
case studies development in different AM functions.
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume VI-4/W1-2020, 2020
3rd BIM/GIS Integration Workshop and 15th 3D GeoInfo Conference, 7–11 September 2020, London, UK
This contribution has been peer-reviewed. The double-blind peer-review was conducted on the basis of the full paper.
https://doi.org/10.5194/isprs-annals-VI-4-W1-2020-77-2020 | © Authors 2020. CC BY 4.0 License.
81
Table 1. Articles organised by type of approach implemented by the authors and Asset Management function.
BIM-oriented approaches GIS-oriented approaches GIS/BIM integrated approaches
Strategic level
Risk Management (Love et al., 2017b) (Nakamura et al., 2019); (Ogie et al., 2018); (Ajmar et al., 2017);
(Pfeiffer et al., 2017); (Ward et al., 2017); (Kruel, 2016); (Inanloo et
al., 2016); (Bhamidipati et al., 2016); (Goyal et al., 2016); (Fekete et
al., 2015); (Sherly et al., 2015); (Espada et al., 2015); (Baah et al.,
2015); (Scaini et al., 2014); (Kim et al., 2013)
(Edmondson et al., 2018)
Sustainability M. (Heaton and Parlikad, 2019) (Nakamura et al., 2019); (Kuller et al., 2019); (Christman et al., 2018);
(Torres-Machi et al., 2018); (Chatziioannou and Álvarez-Icaza, 2017);
(Han et al., 2016); (Ward et al., 2014); (Zhang et al., 2013)
Financial M. (Love et al., 2017b) (Feliciano et al., 2014); (Ward et al., 2014); (Zhang et al., 2013) (Love and Matthews, 2019)
Value M. (Heaton and Parlikad, 2019); (Love et al.,
2015) (Christman et al., 2018); (Loren-Méndez et al., 2018); (Ogie et al.,
2018); (Chatziioannou and Álvarez-Icaza, 2017) (Love and Matthews, 2019)
Quality M. (Pfeiffer et al., 2017); (Ward et al., 2017); (Bhamidipati et al., 2016);
(Goyal et al., 2016); (Santiago-Chaparro et al., 2013) (Love et al., 2018)
Tactical level
Resilience M. (Love et al., 2017a); (Krystallis et al.,
2016) (Nakamura et al., 2019); (Ogie et al., 2018); (Ajmar et al., 2017);
(Pfeiffer et al., 2017); (Ward et al., 2017); (Kruel, 2016); (Han et al.,
2016); (Fekete et al., 2015); (Espada et al., 2015); (Scaini et al., 2014);
(Sitzenfrei and Rauch, 2014); (Kim et al., 2013)
(Edmondson et al., 2018)
Life Cycle Costing (Love et al., 2017a) (Torres-Machi et al., 2018); (Zhang et al., 2013)
Energy M. (Feliciano et al., 2014); (Zhang et al., 2013)
Property M. (Zhang et al., 2018); (Delgado et al.,
2017) (Feliciano et al., 2014)
Facility M. (Zhang et al., 2018); (Smyth et al., 2017);
(Love et al., 2016); (Krystallis et al.,
2016); (Love et al., 2015); (Bosch et al.,
2015)
(Kuller et al., 2019); (Campbell et al., 2019); (Li et al., 2018); (Torres-
Machi et al., 2018); (Inanloo et al., 2016); (Goyal et al., 2016); (Sherly
et al., 2015); (Espada et al., 2015); (Kraus et al., 2014); (Sitzenfrei and
Rauch, 2014); (Ward et al., 2014); (Zhang et al., 2013)
(Zadeh et al., 2019); (Lee et al., 2019);
(Edmondson et al., 2018); (Love et al.,
2018); (Sankaran et al., 2018); (Aziz et al.,
2017); (Guillen et al., 2016)
Operational level
Commissioning M.
Project M. (Abdirad and Dossick, 2020); (Ram et al.,
2019); (Heaton and Parlikad, 2019);
(Saldanha, 2019); (Parlikad and Catton,
2018); (Love et al., 2017b); (Smyth et al.,
2017); (Love et al., 2016)
(Han et al., 2016) (Love and Matthews, 2019); (Hoeber and
Alsem, 2016)
Data M. (Abdirad and Dossick, 2020); (Ram et al.,
2019); (Dell’Acqua et al., 2018); (Zhang
et al., 2018); (Delgado et al., 2017);
(Love et al., 2016); (Bosch et al., 2015);
(Hassan Ibrahim, 2013)
(Kuller et al., 2019); (Campbell et al., 2019); (Li et al., 2018); (Loren-
Méndez et al., 2018); (Pfeiffer et al., 2017); (Fekete et al., 2015);
(Sherly et al., 2015); (Kraus et al., 2014); (Ward et al., 2014); (Zhang
et al., 2013); (Santiago-Chaparro et al., 2013); (Kim et al., 2013)
(Zadeh et al., 2019); (Lee et al., 2019);
(Edmondson et al., 2018); (Love et al.,
2018); (Sankaran et al., 2018); (Aziz et al.,
2017); (Guillen et al., 2016); (Hoeber and
Alsem, 2016)
Condition Inspec. &
Monitor (Delgado et al., 2017); (Love et al., 2016) (Baah et al., 2015) (Lee et al., 2019); (Edmondson et al.,
2018)
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume VI-4/W1-2020, 2020
3rd BIM/GIS Integration Workshop and 15th 3D GeoInfo Conference, 7–11 September 2020, London, UK
This contribution has been peer-reviewed. The double-blind peer-review was conducted on the basis of the full paper.
https://doi.org/10.5194/isprs-annals-VI-4-W1-2020-77-2020 | © Authors 2020. CC BY 4.0 License.
82
5. CONCLUSIONS
The analyses presented in the previous paragraphs provide the
basis for identifying a number of knowledge gaps that could
underpin a research agenda in GeoBIM for infrastructure AM
summarised in the following three groups.
Data management:
• the production of data is often an onerous process and does
not always provide quality outcomes as updated and sound
as-built models;
• as-built models are seldom available, although they would
allow the preservation of a significant amount of data to be
employed in the following stage of the integration and use
of the asset; and
• data are not always reliable and accurate, therefore a deeper
attention to data quality is required.
Interoperability and integration:
• software interoperability is rarely possible for operations
with available data in infrastructure AM. There is a big issue
relating to the BIM/GIS data exchange: typically, between
IFC to CityGML format. Standardised procedures should be
defined to further automate the information exchange; and
• there is a serious issue relating to integration of the existing
information management systems, employed for AM, with
the GeoBIM approach.
Process and service management:
• although literature is limited, advantages of the BIM/GIS
integration have been addressed in Facility and Data
management. However, coupling the potential of BIM and
GIS could leverage strategic and tactical/operational
features of the two approaches;
• detailed information and geometric modelling capabilities
of BIM and advanced data integration and the management
potential of GIS could be leveraged in AM functions where
multi-scale and cross-disciplinary problems arise, such as
Risk and Resilience Management;
• enhanced interoperability and data management capabilities
could improve Condition Inspection & Monitoring, Facility
Management and Life Cycle Costing operations;
• system scalability and, again, data management capabilities
can be harnessed for Energy Management; and
• as result of the literature critical review, BIM and GIS
individually demonstrate effective tools for AM of linear
infrastructure. A fruitful asset class on which to test the
BIM/GIS integration potential might be Railways, where to
date no references can be found.
These issues – which developed specifically with a
GeoBIM/AM/Infrastructure context – reflect those encountered
in wider GeoBIM research (Ellul, 2018), perhaps renewed
additionally complex due to the addition of AM into the mix. A
collaborative effort towards their resolution – to the benefit of the
wider GeoBIM community - is certainly to be encouraged.
ACKNOWLEDGEMENTS
The authors would like to thank Ordnance Survey GB
(https://www.ordnancesurvey.co.uk) and 1Spatial
(https://1spatial.com/) for sponsoring the publication of this
paper.
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