A review of risk management through BIM and BIM-related technologies

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DOI: 10.1016/j.ssci.2015.12.027
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Special Issue Article: Risk and land-use
A review of risk management through BIM and BIM-related technologies
Yang Zou
a,
, Arto Kiviniemi
b
, Stephen W. Jones
a
a
School of Engineering, University of Liverpool, Brownlow Hill, Liverpool L69 3GH, UK
b
School of Architecture, University of Liverpool, Leverhulme Building, Liverpool L69 7ZN, UK
article info
Article history:
Received 28 April 2015
Received in revised form 15 December 2015
Accepted 31 December 2015
Available online 23 January 2016
Keywords:
BIM (Building Information Modelling)
Digital technology
Risk management
BIM-based risk management
Construction safety
abstract
Risk management in the AEC (Architecture, Engineering and Construction) industry is a global issue.
Failure to adequately manage risks may not only lead to difficulties in meeting project objectives but also
influence land-use planning and urban spatial design in the future growth of cities. Due to the rapid
development and adoption of BIM (Building Information Modelling) and BIM-related digital technologies,
the use of these technologies for risk management has become a growing research trend leading to a
demand for a thorough review of the state-of-the-art of these developments. This paper presents a sum-
mary of traditional risk management, and a comprehensive and extensive review of published literature
concerning the latest efforts of managing risk using technologies, such as BIM, automatic rule checking,
knowledge based systems, reactive and proactive IT (information technology)-based safety systems. The
findings show that BIM could not only be utilised to support the project development process as a sys-
tematic risk management tool, but it could also serve as a core data generator and platform to allow other
BIM-based tools to perform further risk analysis. Most of the current efforts have concentrated on inves-
tigating technical developments, and the management of construction personnel safety has been the
main interest so far. Because of existing technical limitations and the lack of ‘‘human factor” testing,
BIM-based risk management has not been commonly used in real environments. In order to overcome
this gap, future research is proposed that should: (1) have a multi-disciplinary system-thinking, (2) inves-
tigate implementation methods and processes, (3) integrate traditional risk management with new tech-
nologies, and (4) support the development process.
Ó2016 Elsevier Ltd. All rights reserved.
1. Introduction
The AEC (Architecture, Engineering and Construction) industry
has witnessed a rapid development all around the world, especially
in developing countries, during the last few decades – large-scale
projects have become widespread and international, new project
delivery methodologies are being adopted, design theory and tools
are constantly improving, creative and new approaches, methods,
and materials of construction are being introduced (Bryde et al.,
2013). AEC projects such as buildings, infrastructure systems and
plants are part of the scope of urban spatial planning and design,
and have an immediate impact on and a direct relation to the
accommodation of land use for the future growth of cities
(Colding, 2007). However, high accident rates and hazardous activ-
ities in the AEC industry not only lead to a poor reputation but pose
a threat to its future innovation and evolution. The scope of a risk is
very broad and consists of issues such as damage or failure of
structures, injury or loss of life, budget overruns, and delays to
the construction schedule, which are caused by various reasons
such as design deficiency, material failure, inexperienced opera-
tives, and weak management. For instance, in the United States,
503 bridge collapses were reported between 1989 and 2000
(Wardhana and Hadipriono, 2003), and according to official
records over 26,000 workers lost their lives on construction sites
from 1989 to 2013 (Zhang et al., 2013). It was estimated that over
60,000 on-site fatal accidents happen every year globally (ILO,
2005). In China, though the number of construction supervision
companies has increased from 52 in 1989 to 5123 in 2000 (Liu
et al., 2004), unwanted hazards related to safety, time, and cost
were observed frequently due to poor risk management (Tam
et al., 2004).
An AEC project starts with planning and design followed by the
construction stage lasting for months or years, and eventually the
project will come into the operation period that may last for dec-
ades before demolition. Different risks may be present in each of
the different stages of the project and product lifecycle. There are
a wide range of risks that may lead to hazards. In recent years, with
the rapid development of society, risks are gradually growing
because of the increasing structural complexity and project size,
http://dx.doi.org/10.1016/j.ssci.2015.12.027
0925-7535/Ó2016 Elsevier Ltd. All rights reserved.
Corresponding author.
E-mail address: Yang.Zou@liverpool.ac.uk (Y. Zou).
Safety Science 97 (2017) 88–98
Contents lists available at ScienceDirect
Safety Science
journal homepage: www.elsevier.com/locate/ssci
and the adoption of new and complex construction methods (Shim
et al., 2012). To reduce the possibility of these hazards occurring
and to achieve project goals successfully, there is a high demand
for managing risks effectively throughout a project’s life cycle.
However, the implementation of traditional risk management is
still a manual undertaking, and the assessment is heavily reliant
on experience and mathematical analysis, and decision making is
frequently based on knowledge and experience based intuition,
which leads to decreased efficiency in the real environment
(Shim et al., 2012). In response to these problems, there is cur-
rently a new research trend of utilising Building Information Mod-
elling (BIM) and BIM-related tools to assist in early risk
identification, accident prevention, risk communication, etc.,
which is defined as ‘‘BIM-based risk management” in this paper.
The paper conducts a critical and extensive review on these new
developments. It firstly presents an overview of the fundamentals,
process, and challenges of the traditional risk management. This
paper further moves on to discuss the state-of-the-art of the use
of BIM and BIM-related technologies for risk management and out-
lines the existing challenges and gaps that slow down or prevent
its broad adoption. The last part of the paper discusses combining
traditional methods with new technologies and identifies research
areas where additional research is needed in the future.
2. Research approach
2.1. Motivation and aim
The literature includes numerous studies describing the devel-
opment of BIM and BIM-related technologies for managing particu-
lar risks (Chen and Luo, 2014; Hadikusumo and Rowlinson, 2004;
Zhang and Hu, 2011; Zhang et al., 2013). Nearly all reviews
(Bryde et al., 2013; Eastman et al., 2009; Forsythe, 2014;
Hartmann et al., 2008; Zhou et al., 2012) partially summarise the
application area, development and shortcomings of applying these
technologies, and cover only one or several aspects separately.
Many papers (Ahmed et al., 2007; Jannadi and Almishari, 2003;
Vrouwenvelder et al., 2001; Zou et al., 2007) concentrate on review-
ing traditional risk management methods and other publications
(Azhar, 2011; Eastman et al., 2011; Tomek and Mate
ˇjka, 2014)
partially summarise the benefits and risks of implementing BIM
in projects. However, to the authors’ knowledge there is no compre-
hensive overview of recent research on BIM-based risk manage-
ment as a comprehensive whole and no studies focusing on the
relationship between digital technologies and the traditional meth-
ods for managing risk. The aim of this review is to close this gap,
identify the obstacles of BIM-based risk management as well as
foster research interests for the future.
2.2. Methodology
To review BIM-based risk management critically, a three-step
approach was conducted. The topic of ‘‘risks of implementing BIM”
and papers that are not published in English are not within the
scope of this review.
In the first step, the fundamentals, general process, and main
challenges of traditional risk management are summarised
through an extensive literature review and several expert inter-
views for comprehensive understanding of the relation between
the traditional methods and BIM-based risk management. The pro-
cess identifies a set of keywords for data collection as the basis for
the next step. The main keywords are, for example, ‘‘BIM”, ‘‘build-
ing information model”, ‘‘risk”, ‘‘risk assessment”, ‘‘risk analysis”,
”risk management”, ‘‘knowledge management”, ‘‘safety”, ‘‘quality”,
‘‘time”, ‘‘cost”, and ‘‘budget”. In the second step these keywords
were applied to a web search in online academic publication data-
bases, i.e. ‘‘Web of Science”, ‘‘Engineering Village”, ‘‘Scopus”, and
‘‘Google Scholar”, for collecting academic and applied publications
related to this topic. Then the state-of-the-art of these technologies
is classified and surveyed as follows: (1) BIM, (2) automatic rule
checking, (3) knowledge based systems, (4) reactive IT-based
safety systems (i.e. database technology, VR, 4D CAD, GIS), and
(5) proactive IT-based safety systems (e.g. GPS, RFID, laser scan-
ning). The scope of the survey includes articles in leading journals
of this area (e.g. Safety Science,Automation in Construction,Interna-
tional Journal of Project Management,Journal of Computing in Civil
Engineering,Information Technology in Construction,Reliability Engi-
neering & System Safety), publications from conference proceedings
and other sources of professional associations, standard commit-
tees (e.g. HSE, ISO) and authorities. In the third step, all publica-
tions are analysed critically and compared with the traditional
risk management methods to identify current obstacles and future
work to close these gaps.
3. Background
3.1. The fundamentals of risk management
The term ‘‘risk” was known in the English language from the
17th century and was derived from an original meaning to run into
danger or to go against a rock (McElwee, 2007). Today the concept
of risk is adopted in many different fields and with a variety of dif-
ferent words, such as ‘‘hazard”, ‘‘threat”, ‘‘challenge”, or ‘‘uncer-
tainty”. In the AEC industry, risks have a two-edged nature, e.g.
‘‘the likelihood of unwanted hazards and the corresponding conse-
quences”(Zou et al., 2007), ‘‘the likelihood and consequence of risks
(Williams, 1996), ‘‘a combination of the likelihood and consequences
of the hazard”(Vrouwenvelder et al., 2001).
Risk management is a system aiming to recognise, quantify, and
manage all risks exposed in the business or project (Flanagan and
Norman, 1993). PMBOK
Ò
(Project Management Body of Knowl-
edge) describes it as a process in relation to planning, identifying,
analysing, responding, and monitoring project risks and one of
the ten knowledge areas in which a project manager must be com-
petent (PMI, 2004). The International Organization for Standard-
ization (ISO, 2009) defines the process of risk management
involving applying a systemic and logical method for establishing
the context, creating a communication and consultation mecha-
nism, and constructing risk management identification, analysis,
evaluation, treatment, monitoring, and recording in a project. In
accordance with these definitions, risk management in the AEC
context is a logical, systematic, and comprehensive approach to
identifying and analysing risks, and treating them with the help
of communication and consultation to successfully achieve project
goals. The systematic process includes risk identification, analysis,
evaluation, treatment, monitoring and review (Banaitiene and
Banaitis, 2012; ISO, 2009; Zou et al., 2007), where risk identifica-
tion aims to find out the range of potential risks and risk analysis
plays a core role in the whole process. When risks cannot be elim-
inated, early and effective identification and assessment of risks
become necessary for effective risk management in a successful
project (Zou et al., 2007). All activities of a project involve risks
(ISO, 2009) and there is an immediate and direct relationship of
objectives between the whole project and risk management.
A set of techniques has been developed to identify, analyse and
evaluate risks. The techniques, according to ISO (2009), can be
divided into qualitative and quantitative analysis. The former
includes Delphi, check lists, strength–weakness–opportunity–thre
ats (SWOT) analysis, risk rating scales, etc., while the latter
includes environmental risk assessment, neural networks (NN),
Y. Zou et al. / Safety Science 97 (2017) 88–98 89
row tie analysis, reliability centred maintenance, risk indices, and
others. However, though the above methods are important tech-
niques for risk management, they are confined to static control
management and play only a limited role in practice (Zhang
et al., 2014). The implementation of traditional risk management
is still a manual undertaking, the assessment is heavily reliant on
experience and mathematical analysis, and the decision making
is frequently based on knowledge and experience based intuition,
which always leads to a decreased efficiency in the real environ-
ment (Shim et al., 2012).
3.2. The general process of risk management
Based on a review of the literature, expert interviews, and the
authors’ own experience, the current general risk management
framework used in the UK AEC industry is summarised in Fig. 1.
The framework prescribes a long-term risk management strategy
and a process that allows participants to work collaboratively to
manage risks in a systematic way. The core philosophy of this
method, defined in the Risk Mitigation Model, is that the main
scope for identifying and mitigating risks should be as early as pos-
sible, especially in the design or planning phases, which is regu-
lated in the UK’s Construction Design and Management (CDM)
Regulations 2007 (HSE, 2007). Ideally most of the foreseeable risks
should be ‘‘designed out” during the planning or design stages, and
the residual risks should be managed during the construction and
subsequent phases.
However, some challenges in the above process are: (1) in-time
knowledge capture and analysis, (2) the management of multi-
disciplinary knowledge and experience, and (3) effective commu-
nication environment. Valuable knowledge and experience are
gained from previous projects and this can be used to contribute
to future work. In this case, the effective management of this large
database of human knowledge and experience, as well as flexible
and accurate data extraction, become a precondition for the suc-
cess of risk management. As the project is handed over from
designer to contractor, and then from contractor to the client, peo-
ple will normally leave the project after completing their tasks and
large amounts of risk information may be lost if it is not properly
recorded and communicated to other project participants (Kazi,
2005).
3.3. Information and Communication Technologies (ICT) for risk
management
To overcome these obstacles, ICT, e.g. BIM, 4D CAD, and Virtual
Reality (VR), has been applied in the AEC industry to manage risks.
For instance, construction safety risk planning and identification is
an issue addressed by 3D/4D visualisation (Hartmann et al., 2008).
BIM could help automatically detect physical spatial clashes (Chiu
et al., 2011) and specific requirements of building codes could be
interpreted to machine-read rules and checked automatically in
Industry Foundation Classes (IFC) information models (Eastman
et al., 2009). Li et al. (2013) presented a proactive monitoring sys-
tem using Global Positioning System (GPS) in combination with
Fig. 1. General risk management framework.
90 Y. Zou et al. / Safety Science 97 (2017) 88–98
Radio Frequency Identification (RFID) to improve the safety of
blind lifting of mobile/tower cranes. The next section will review
and discuss these developments critically in detail.
Two reasons could explain the increasing interest and adop-
tion of ICT for risk management. The first reason is that as the
industry has benefited from salient technical advantages of BIM
and other digital technologies, a natural consequent is to investi-
gate their possibilities in risk management. These new techniques
could not only provide new design tools and management meth-
ods (Eastman et al., 2011) but significantly facilitate the collabo-
ration, communication, and cooperation for both within and
between organisations (Dossick and Neff, 2011), which are essen-
tial requirements for managing risks successfully. The second rea-
son comes from a strong thrust from the government policy
makers who have realised the importance of integrating ICT with
risk management. Evidence of this is the new version of CDM reg-
ulations that will cover ICT such as BIM after 2015 (Joyce and
Houghton, 2014) replacing the older version that was introduced
in the UK initially in 1996 for improving safety and risk
management.
4. Survey of BIM and BIM-related technologies for managing
risks
The state-of-the-art of the use of BIM and BIM-related tech-
nologies for risk management is summarised in this section. The
technologies referred here include BIM, automatic rule checking,
knowledge based systems, reactive and proactive safety systems
based on information technology. There is a distinct difference
between reactive and proactive safety systems for risk manage-
ment. Forsythe (2014) and Teizer et al. (2010) pointed out reac-
tive systems using information technologies such as VR, 4D
CAD, and GIS seldom use real-time data and need a post data col-
lection processing effort for analysis, while in contrast proactive
technologies can collect and analyse real-time data, and provide
real-time warning and immediate feedback to construction site
about dangers in time. It has been found that BIM, on one hand,
can be used as a systematic risk management tool in the develop-
ment process and, on the other, can perform as a core data gener-
ator and platform to allow other BIM-related tools for further risk
analysis, where most of these technologies can be used interac-
tively in related investigations.
4.1. Managing risks through BIM
Over the last few years, with the rapid development of theory
and computer applications, BIM has achieved a remarkable
awareness in the AEC industry and there is a significant increase
of the adoption of BIM to support the planning, design, construc-
tion, operation and maintenance phases (Volk et al., 2014).
Instead of being just considered as a technology, BIM is becoming
a systematic method and process that is changing the project
delivery (Porwal and Hewage, 2013), designing (Liu et al., 2014),
and the communication and organisational management of con-
struction (Hardin, 2011). Though most papers utilising BIM as
an advanced tool to manage project risks such as design errors,
quality, and budget do not often refer to risk management inten-
tionally, the process of applying BIM can be seen, to some extent,
as a systematic way for managing risks. Examples are presented
in Table 1.
In the planning and design stages, one of the main risks is how
the design aligns with the determined project feasibility, secured
budget, and established governance regime (Miller et al., 2001).
This is an area where BIM has the potential to manage the risks.
For example, the visualisation of preliminary design by 3D/4D
Table 1
Examples for applying or developing BIM for risk management.
Functionality Benefits for risk management Research Practice
3D visualisation Facilitating early risk identification and risk communication Hartmann et al. (2008) Liu et al. (2014), Shim et al. (2012)
Clash detection Automation of detecting physical conflicts in model Hartmann et al. (2008), Tang et al. (2011) Chiu et al. (2011), Liu et al. (2014)
4D construction scheduling/planning Facilitating early risk identification and risk communication;
improving construction management level
Hardin (2011), Hartmann et al. (2008), Whyte (2002) Chiu et al. (2011), Liu et al. (2014)
5D cost estimation or cash flow modelling Planning, controlling and managing budget and cost reasonably Hardin (2011), Hartmann et al. (2008),
Marzouk and Hisham (2014), Whyte (2002)
Motawa and Almarshad (2013)
Construction progress tracking Improving management level for quality, safety, time, and budget Bhatla et al. (2012), Eastman et al. (2011)
Safety management Reducing personnel safety hazards Teizer (2008), Whyte (2002)
Space management Improving the consideration of space distribution and
management in design
Hartmann et al. (2008), Kim et al. (2012)
Quality control Improving construction quality Chen and Luo (2014)
Structural analysis Improving structural safety Lee et al. (2012b), Sacks and Barak (2008),
Shim et al. (2012)
Liu et al. (2014)
Risk scenario planning Reducing personnel safety hazards Azhar (2011), Hardin (2011) Hartmann et al. (2012)
Operation and maintenance (Q&M),
facility management (FM)
Improving management level and reducing risks Becerik-Gerber et al. (2011), Volk et al. (2014)
Interoperability Reducing information loss of data exchange Ji et al. (2013), Laakso and Kiviniemi (2012)
Collaboration and communication
facilitation
Facilitating early risk identification and risk communication Dossick and Neff (2011), Grilo and Jardim-Goncalves (2010),
Porwal and Hewage (2013)
Urban planning and design Integrating planning and design of urban space and AEC projects;
facilitating land-use planning, design and management
Kim et al. (2011), Lee et al. (2012a), Rajabifard et al. (2012) Lee et al. (2012a)
Y. Zou et al. / Safety Science 97 (2017) 88–98 91
models could help engineers build and modify the model quickly in
a parametric way to meet the stakeholders’ requirements
(Hartmann et al., 2008). The short videos or virtual walkthroughs
which simulate the view of a person walking through the building
can rapidly improve stakeholders’ understanding of the project
(Whyte, 2002). Meanwhile, neutral data formats such as the IFC
that store standard and customised data for all project elements
could provide an interoperable digital representation of all project
elements enabling interoperability between BIM software applica-
tions (Laakso and Kiviniemi, 2012), which could increase the
repeated use of data and reduce the possibility of errors.
At the construction stage, there is often a huge pressure for the
construction team to complete the project safely within budget
and schedule, and various risks and uncertainties exist in this per-
iod. To identify construction risks at an early stage and optimise
the construction sequences, Chiu et al. (2011) conducted a clash
detection and a 4D simulation of the construction of a steel bridge.
Chen and Luo (2014) extended the 4D model to cover quality man-
agement based on construction codes and established a quality
control model in a product, organisation and process (POP) data
definition structure, which was used and validated in the construc-
tion of the Wuhan International EXPO centre. In addition, Marzouk
and Hisham (2014) used BIM’s ability of cost estimation to develop
an application that integrates BIM with Earned Value (EV) for cost
and schedule control, and determines the project status at specific
reporting dates for infrastructure bridges.
It has also been found in this review that though the majority of
efforts still focus on applying BIM to the design and construction
phase, BIM can also be used in other processes and phases, e.g.
facility management (Becerik-Gerber et al., 2011), maintenance
management (Volk et al., 2014), and demolition (Cheng and Ma,
2013). In addition, a BIM-based collaboration and communication
environment could naturally facilitate the early risk identification
and mitigation (Dossick and Neff, 2011; Grilo and Jardim-
Goncalves, 2010).
4.2. Knowledge based systems
In the AEC industry, every project produces valuable knowledge
and experience which can contribute significantly to managing
risks in future projects. It is essential to manage this information
properly and communicate it effectively in all stages of the whole
project lifecycle (Tah and Carr, 2001). This idea has been recog-
nised and adopted for a long time by researchers to manage project
risks. For example, Total-Safety (Carter and Smith, 2006)isa
method statement development module within an ICT tool that
could assist engineers to formulate method statements with a high
level of risk identification by extracting safety information from a
knowledge based database. When a construction method is chosen,
the tool can return all known risks associated with different tasks
as the knowledge basis for further risk assessment. Similarly,
Cooke et al. (2008) proposed a web-based decision support pro-
gram named ToolSHeD to integrate assessment of safety risk into
design process. The principle of ToolSHeD is to structure the
knowledge obtained from industry standards, national guidelines
and codes of Australia, and other information sources, and employ
this knowledge for assessing risks in complicated situations of
buildings.
The integration of BIM and knowledge based systems has been
seen as a new trend. Deshpande et al. (2014) proposed a new
method to capture, extract, and store information and knowledge
from BIMs, and presented a framework for classification and dis-
semination of the knowledge. To strengthen its practical applica-
tion, Ho et al. (2013) developed a BIM-based Knowledge Sharing
Management (BIMKSM) system that could enable managers and
engineers to share knowledge and experience in the BIM environment.
Aiming at managing safety risks in design, Qi et al. (2011)
developed a dictionary of construction worker suggestions and a
constraint model to store the formalised suggestions. Then in the
BIM environment, designers could utilise rule checking software
for identifying safety risks during the planning and design phases,
and mitigating risks and optimising their designs. The system con-
sists of three parts: BIM as the main information input, a knowl-
edge based system, and a risk identification module. Motamedi
et al. (2014) integrated the use of knowledge management (KM)
and BIM to investigate an approach for detecting failure root-
cause which could help facility management (FM) technicians
identify and solve problems from their cognitive and perceptual
reasoning. Integrated with BIM, a Computerised Maintenance
Management System (CMMS) was developed to store inspection
and maintenance data. In addition, a knowledge based BIM system
was presented by Motawa and Almarshad (2013) to capture and
store various types of information and knowledge created by dif-
ferent participants in the construction project in order to support
decision making for building maintenance.
4.3. Automatic rule checking
In definition, the term Automatic Rule Checking is the use of a
computer program to assess a design based on objects’ configura-
tion (Eastman et al., 2009) and its purpose is to encode rules and
criteria by interpretation and thus building models could be
checked against these machine-read rules automatically with
results, for example, ‘‘pass”, ‘‘fail”, ‘‘warning”, or ‘‘unknown”
(Borrmann et al., 2009).
Regulations and rules written by experts have traditionally
been comprehended, interpreted and used in a manual way. Thus,
these rules are sometimes conflictive and incomplete, and the cor-
responding implementation is often limited by people’s under-
standing, interpretation, and reasoning capability. To computerise
this process and improve the effectiveness, the research of auto-
matic code checking or rule compliance started in the 1960s. Soon
afterwards, a lot of effort was put into interpreting particular
requirements to computerised codes, logically structuring and
managing rules, and developing rule-based systems (Fenves,
1966; Fenves et al., 1995; Garrett and Fenves, 1987; Rasdorf and
Lakmazaheri, 1990). In the late 1990s, due to the fast growth
rule-based systems for building models, the development of IFCs
brought on the initial exploration of building model schema for
checking building codes. This review has observed three develop-
ment directions in the area of automatic rule checking during the
last two decades – (1) building design codes compliance, (2) con-
struction safety checking, and (3) special requirements checking,
which will be discussed further in detail below. A comprehensive
review, which introduced the main steps and software platforms
of automatic rule checking, was reported by Eastman et al. (2009).
The most common application of rule checking is to ensure the
design work is compliant with numerous building codes which are
normally known as the minimum standards for construction
objects such as buildings and infrastructure projects. To comput-
erise this work, two major activities are needed to achieve this
goal: (1) to formalise the building code and BIM into building rule
models and building design representation models respectively;
and (2) to implement both models in computer programs and exe-
cute rule objects over design objects in compliance checking auto-
matically (Yang and Xu, 2004). Substantial efforts in this area have
been made in recent years. For example, Delis and Delis (1995)
proposed a method which could encode fire code requirements
in a knowledge based system for analysing the performance of fire
safety in the completed building design. Balachandran et al. (1991)
developed an approach to processing non-measurable code provi-
sions for verifying building designs automatically. Solihin (2004)
92 Y. Zou et al. / Safety Science 97 (2017) 88–98
developed the e-PlanCheck system by using the IFC model and
Express Data Manager (EDM) for assessing the code compliance
in Singapore. One of the latest efforts in this area is an on-going
project in the US funded by Fiatech to develop AutoCodes expect-
ing to improve automatic code checking capability for BIM stan-
dards and guidelines, and US building model codes (Fiatech, 2013).
The second development direction is to check construction
safety rules. To prevent any human safety accidents on site, it is
essential to identify and mitigate these risks in design, and inspect,
monitor and manage safety in construction. Hence the design stage
is the best opportunity to mitigate most of these risks if potential
hazards could be well identified and planned, and corresponding
measures to control these risks can be chosen correctly (Bansal,
2011). Yi and Langford (2006) collected and analysed historical
safety records and proposed a theory that could estimate a pro-
ject’s risk distribution. Sulankivi et al. (2013) presented a theory
to identify safety risks which are unknowingly built into the con-
struction activities at the design stage and developed a BIM-
based automatic safety rule-checking prototype. The approach
works by simulating the construction sequences and tasks with
embedded safety rules. Aiming at fall protection, Zhang et al.
(2013) formalised the fall protection rules of the Occupational
Safety and Health Administration (OSHA) and other best practices
into a table-based safety rule translation algorithm, and imple-
mented a rule-based checking system in BIM to plan and simulate
safety issues at an early stage. The feasibility has been shown by
implementing this approach in Tekla Structures.
The last application direction of development is for checking
specific requirements of buildings, such as the circulation prob-
lems, space requirements, and special site considerations. For
instance, Han et al. (2002) presented a hybrid method that used
encoding prescriptive-based provisions and supplemented them
with a performance-based approach to facilitate conformance
and applicability analysis for accessibility. Lee (2010) developed
a new approach to checking occupant circulation rules automati-
cally in the US Courts Design Guide, which could assist circulation
rule checking in the development processes of a courthouse’s
design. Lee et al. (2010) proposed a computational approach called
the Universal Circulation Network (UCN) for checking walking dis-
tances between buildings by implementing a length-weighted
graph structure for building models, and developed a plug-in on
top of the Solibri Model Checker.
4.4. Safety risk management through reactive IT-based safety systems
The AEC industry is still faced with a particular challenge of
high accident rates – over 6 percentage in Hong Kong for instance
(OSHC, 2008). To detect health and safety (OHS) risks in time and
mitigate them before any hazards occur, reactive IT-based safety
systems have been used in conjunction with BIM to achieve this
goal. Forsythe (2014) and Zhou et al. (2012) summarised these
technologies including, for example, database technology, Virtual
Reality (VR), 4D CAD, Geographic Information Systems (GIS), which
are discussed in this sub-section.
4.4.1. Database technology
Experience and knowledge learned from past accidents provide
a better perception to prevent hazards in future work (Gambatese
et al., 2005). An obvious step from this is database technology that
could be used to store valuable knowledge, capture accurate infor-
mation and then intelligently extract them based on specific selec-
tion criteria (Forsythe, 2014). For example, Imhof (2004) collected
360 cases of bridge failures and established an online database to
help learn from past accidents, analyse the risk distribution and
summarise the main risk factors that led to bridge collapse, which
allows a better understanding of the mechanism of an accident and
a better insight of how to prevent hazards in the future. Yu (2009)
developed a knowledge based decision support model on the basis
of knowledge representation and reasoning features to assist cli-
ents to evaluate competence of potential designers, principal con-
tractors, and CDM coordinators. Furthermore, to improve the
performance and capability, an enhanced online database called
Construction Safety and Health Monitoring (CSHM) system was
developed to enable remote access, speedy data collection and
retrieval, and expert communication (Cheung et al., 2004).
4.4.2. Virtual reality
Virtual Reality (VR) is an important area in current BIM research
and vice versa (Gu and London, 2010). Conceptually, VR is a virtual
system that consists of a computer capable of real-time animation,
controlled through a group of equipment for simulating physical
presence in places in the real world (Steuer, 1992). VR has been
used to provide a 3D, virtual and interactive computer environ-
ment for training site workers to become aware of identified on-
site safety risks (e.g. (Guo et al., 2012)) and formalising strategies
and measures of potential hazards by simulating the dangerous
scenarios (e.g. (Wang et al., 2014)). Specifically, Guo et al. (2012)
presented a game based interactive multi-client platform for safety
training to improve construction site operation safety. Embedded
with identified hazards, the platform provides a virtual environ-
ment where trainees can learn and practice operating methods
and construction sequences, which closely resemble the real work-
ing on-site environment. The presented platform also encourages
trainees to work collaboratively with others in operating the con-
struction site. Though technological development looks extremely
important in VR for managing safety risks, how these developed
technologies could be adopted and implemented in practice
becomes another concern. Therefore, after summarising the main
factors that may cause construction accidents, Guo et al. (2013)
proposed a conceptual framework to adopt Virtual Prototyping
(VP), consisting of three core components: (1) modelling and sim-
ulation, (2) identification of unsafe factors, and (3) safety training,
to support construction health and safety risk management for
both technicians and workers. For improving the building emer-
gency management, Wang et al. (2014) developed a BIM based vir-
tual environment (BIM-VE) to address two key issues: ‘‘(1) timely
two-way information flow and its applications during the emergency
and (2) convenient and simple way to increase evacuation aware-
ness”. In addition, VR can also be incorporated with database tech-
nology for managing construction safety risks. For example,
Hadikusumo and Rowlinson (2002, 2004) created a design-for-
safety-process (DFSP) tool to aid safety risk identification when
producing the construction plans and schedules in the design
stage. This tool comprises three components: (1) the DFSP data-
base, (2) the virtual reality construction components and pro-
cesses, and (3) virtual reality functions. The DFSP database stores
a full list of common dangerous conditions and actions, local acci-
dent reports and rules. The integration of the VR components and
DFSP database allows users to walk through in a virtual project
environment from a first-person view and to identify safety risks
within construction components and related processes, and to
choose preventative measures for those identified risks.
4.4.3. 4D CAD
Early research of applying four-dimensional computer aided
design (4D CAD) for construction planning to identify potential
problems, mitigate risks, and optimise construction schedule and
processes started in the early 1990s (Heesom and Mahdjoubi,
2004). The core concept of 4D CAD is to add 4D construction sched-
ule information into a 3D model to establish a collaboration and
communication media and clear visual insights of the construction
sequences for the construction team (Koo and Fischer, 2000). It is
Y. Zou et al. / Safety Science 97 (2017) 88–98 93
observed that the most common application of 4D CAD for safety
risk management is to establish an extensive 4D CAD model by
gathering all design data about building objects and construction
processes, activities and sequences, and conduct further risk anal-
ysis on the basis of the model. For instance, Benjaoran and Bhokha
(2010) presented a 4D CAD model to integrate safety risk and con-
struction management. Rule-based algorithms for working-at-
height risks were formalised, interpreted, and visualised into the
model. A rule-based system was then used to extract information
from the 4D CAD model to detect working-at-height risks automat-
ically and forecast necessary measures including safety activities
and requirements. In structural analysis, Hu and Zhang proposed
a new method in their two papers (Hu and Zhang, 2011; Zhang
and Hu, 2011) to analyse safety and conflict by incorporating
BIM, 4D CAD, time-dependent structural analysis, and clash detec-
tion, and then implemented this theoretical solution by developing
an integrated archetypal system named 4D-GCPSU 2009. A group
of researchers from Finland’s VTT Technical Research Centre
demonstrated a BIM-based safety management and communica-
tion system that develops construction procedures and BIM for
4D safety planning, management, and communication, where
BIM and 4D CAD are utilised as the central technologies
(Kiviniemi et al., 2011).
4.4.4. Geographic information systems
While BIM is defined to develop objects’ geometric data into the
maximum level of detail, a Geographic Information System (GIS) is
a collection of environmental information from the macro perspec-
tive (Irizarry and Karan, 2012; Zhou et al., 2012). GIS can be inte-
grated into a Decision Support System (DSS) to monitor and
control safety risks (Cheng et al., 2002). Along a similar line,
Bansal (2011) successfully applied GIS to predict places and activ-
ities where there was an increased likelihood of hazards in a build-
ing project in India because BIM and 4D modelling could not
provide the capability for features like 3D components editing,
topography modelling, geospatial analysis, and generation and
updating of schedules. Bansal and Pal (2007) also proved GIS has
the potential to help cost estimation and visualisation. Recently,
several studies have been conducted to explore how to integrate
BIM and GIS to improve construction site safety risk management
and optimisation. For example, Irizarry and Karan (2012) inte-
grated the use of BIM and GIS and proposed a GIS–BIM model to
assist identification and optimisation of the feasibility for the loca-
tion of tower cranes. In this work, BIM software was first used to
generate geometry information of the construction site, and the
GIS model then extracted data from the BIM to determine the
proper combination of tower cranes for location optimisation.
The analysis output linking to the BIM platform can suggest one
or more possible areas including all supply points and demand.
4.5. Proactive IT-based safety systems
As described in the previous sections, reactive IT-based safety
systems are able to provide 4D simulation and virtual prototyping
to assist safety risk identification and construction safety manage-
ment planning. However, as planning is by nature a predictive pro-
cess established on previous knowledge and experience, the
construction projects have a habit of changing during the dynamic
processes of project lifecycle (Forsythe, 2014). To manage those
unplanned changes and unexpected safety risks, it is important
to track the hazard areas, collect real-time data from the sites for
further analysis, and give immediate warning or feedback to the
active construction workspace before the actual occurrence of haz-
ards, which is what proactive IT-based safety systems could help
(Teizer et al., 2007). To achieve this objective, proactive IT-based
safety systems can be created by combining one or more
information technologies, BIM, and possibly other techniques.
Teizer et al. (2007) and Forsythe (2014) summarised the related
technologies, approaches, their features, and current situation
and development. The core philosophy behind proactive IT-based
safety systems is to create a virtual environment where accurate
positions of both static and moving objects can be tracked, the
corresponding data from the real world can then be collected in
real time and analysed by formalised safety algorithms, and, most
importantly, information of hazards could be delivered in real-time
and effective mitigation measures can be taken in time.
Currently, most efforts of proactive IT-based safety systems
focus on tracking the static and moving objects in particular con-
struction activities such as excavator and crane usage. For example,
Kim et al. (2004) presented a theoretical model of a human-
assisted obstacle-avoidance system with a 3D workspace model,
and a sparse point cloud approach was described for modelling sta-
tic objects or zones which may lead to hazards or have been iden-
tified to have risks. The framework includes algorithms for obstacle
avoidance system as well as for 3D workspace modelling. To apply
this theory, McLaughlin et al. (2004) developed an obstacle detec-
tion system to allow machines to navigate around equipment
safely. Radio frequency wave spectrum technology was applied
by Allread (2009) to warn workers in real time where blind spots
occur for machine operators and when they are in danger. To
improve the safety of blind lifting of mobile/tower cranes, Li
et al. (2013) presented a real-time monitoring system which inte-
grates the use of Radio Frequency Identification (RFID) and Global
Positioning System (GPS). The system can detect the interactive
proximity between unauthorised work or the entrance of person-
nel and the crane. When workers were present within a risk zone,
a warning was sent to the safety management team. Other proac-
tive technologies have been used in this area including, laser scan-
ning (Cheng and Teizer, 2014), remote sensing and actuating
technology (Teizer et al., 2010), and wireless communication
(Wu et al., 2013).
In order to improve the tracking accuracy and reliability, Teizer
et al. (2013) used Ultra-Wideband (UWB) to deal with the indoor
and outdoor settings and to provide the 3D and 4D location values
accurately in real time. To enhance the risk management in large
transit projects, Ding and Zhou (2013) developed a web-based sys-
tem for safety early warning in urban metro construction. From
this review, it has also been observed that sensors receiving pas-
sive warning signals are commonly embedded into Personal Pro-
tective Equipment (PPE), such as safety helmets, hats, and shoes,
for enhancing the portability of these warning devises, e.g.
(Abderrahim et al., 2005; Teizer et al., 2010).
4.6. Implications of BIM-based risk management
The purpose of this section is twofold: (1) to provide an over-
view discussion of BIM-based risk management, and (2) to sum-
marise the shortcomings of related technologies.
The literature shows that BIM and numerous BIM-related digi-
tal technologies have been developed to assist risk management
during a project’s lifecycle. These technologies, discussed in the
previous sub-sections, include BIM, automatic rule checking,
knowledge based systems, reactive and proactive safety systems.
Applications managing some particular risks can be developed
based on either a single technology or a combination of several
technologies as illustrated, for instance, in the 4D-GCPSU 2009 sys-
tem. What can be seen from all of the above efforts is that there has
been an emphasis on identifying and mitigating risks as early as
possible, and managing real-time risks before any occurrences of
hazards. Meanwhile, the findings show that despite considerable
developmental work, most of their focus has been on exploiting
new technologies to mitigate single risks in particular scenarios
94 Y. Zou et al. / Safety Science 97 (2017) 88–98
for design and construction stages, such as the prevention of falling
accidents through automatic rule checking. The management of
construction personnel safety risk is a main interest so far, e.g. in
Sections 4.4 and 4.5.
However, there is a need to point out that most existing studies
are at a conceptual or prototyping stage because of existing limita-
tions. For example, an important challenge for knowledge based
systems is how to ensure the knowledge and experience shared
by a limited number of professionals are complete and ‘‘correct”
information of the potential risks. Though in current AEC projects,
successful project risk management is still heavily reliant on all
participants’ experience and knowledge, as discussed in Section 3.2,
different people have different educational backgrounds, knowl-
edge bases, and project experience, and the process of risk manage-
ment through knowledge sharing is naturally complicated.
Eastman et al. (2009) highlighted three main problems in current
automatic rule checking systems: (1) most common rule checking
systems rely on IFC as input and currently are limited in what they
support; (2) rule checking at the scale of all sections of a project’s
codes is a massive undertaking. A critical problem is how to iden-
tify and verify the potential errors in the rule checking algorithms
and building models; (3) current efforts enable checking the final
state of a design but fail to support its development process.
Though several reactive IT-based safety systems have been applied
for safety risks planning before actual operation, as described in
Section 4.4, a significant shortcoming exists. The planning process
is by nature established on knowledge and experience-based
human assumptions. As construction is a dynamic process which
may last for many years and involves frequently unexpected
changes and unplanned risks, operational risk management cannot
normally fully comply with the original planning. Regarding this
issue, an additional method is to work on a collaborative 4D con-
struction planning platform by collecting as much reliable multi-
discipline knowledge and experience as possible (Zhou et al.,
2009). Another alternative approach is to use proactive technolo-
gies for real-time data collection and treatment, as described in
Section 4.5. However, much of the cited work on proactive systems
is still very young. Some particular hazardous scenarios in, for
example, excavation and lifting have been considered. Meantime,
so far most of these efforts only focus on technical development,
and these technologies have not reached the stage of ‘‘human fac-
tor” testing (Forsythe, 2014). Therefore there is still a long way to
go before the wide use of these new technologies for risk manage-
ment will be common in the workplace.
5. Discussion
An important aspect of this research is to find out challenges
and research gaps in current BIM-based risk management through
a systematic and critical review, which is discussed as follows:
5.1. A multi-disciplinary system-thinking
This review indicates that developing new technologies to assist
with the management of construction safety risks is currently a
popular research topic. However, any AEC project starts with plan-
ning and design followed by the construction stage lasting for
months or years, and eventually the project will come into the
operation period that may last for decades before demolition. Var-
ious types of risks (e.g. structural safety risk, financial risk, environ-
mental risk, supply risk) may be present in the different stages of
the project and product lifecycle. People with different knowledge
background and from different domains may be involved in the
dynamic process of risk management. ISO (2009) stated that ‘‘risk
management is a logic and systematic method”. Hence, it is clear that
the concept of multi-disciplinary system-thinking should be
embedded in the research of BIM-based risk management.
5.2. Implementation method and process
The findings show that despite considerable development work,
much of the focus has been on exploiting and developing new tech-
nologies to treat specific risks in a particular scenario, which were
also mentioned by Zhou et al. (2012) and Forsythe (2014). Since
AEC projects are one-off endeavours with numerous special fea-
tures and risks existing during the whole dynamic process, any
new methods for risk management are valuable when core project
participants start to use these enhanced technologies as part of
their daily work. The complete implementation framework or
method of BIM-based risk management consisting of fragmented
activities and processes are equally important as technical devel-
opments. Finally the people, who work collaboratively in a project
team using these technologies for managing risks, make the pro-
jects successful, and profitable. Based on these observations, an
important research topic is to investigate how BIM and BIM-
related technologies can be implemented in real projects to
achieve their best value.
5.3. Integration of BIM-based and traditional methods for risk
management
Another knowledge gap observed in this review is that there are
nearly no studies focusing on integrating BIM and BIM-related dig-
ital technologies with the traditional methods, processes, and tech-
niques for risk management. Numerous investigations (Hartmann
et al., 2012; Shim et al., 2012; Zhang et al., 2014) have pointed
out that the traditional method is heavily reliant on experience
and multi-disciplinary knowledge, and common risk assessment
techniques include Fault Tree Analysis (FTA) (Suresh et al., 1996),
decision trees (Dey, 2002), and neural networks (NN)
(Khoshgoftaar and Lanning, 1995), etc. These general methods
have been commonly applied by the AEC industry and play a sig-
nificant role in real projects. Clearly, there is a need to combine
BIM-based and traditional risk management to improve practical
applicability. The potential and benefits have been proved by sev-
eral instances. For example, Shim et al. (2012) converted the tradi-
tional risk management method into visual information in a
visualisation environment to improve the efficiency for practition-
ers in dynamic risk management in terms of schedule, cost and
safety to assist the design and construction and management of a
challenging cable stayed bridge project. Another study, from a
‘‘technology pull” perspective, aligned BIM with risk management
into a large infrastructure project to test its practical performance
(Hartmann et al., 2012).
5.4. BIM-based risk management as part of the development process
Undoubtedly risks may be present in the different stages of the
project and product lifecycle and the performance of risk manage-
ment has a direct influence on whether the project can be fulfilled
successfully on-time and within budget. In the UK, the CDM rules
are a compulsory legislation requirement that indicates all risk
analysis for a project starts with the designer. It is the designer
who has to assess the risks that may occur during the construction,
use of the project, maintenance (including equipment replace-
ment), and demolition. It is the responsibility of the designer to
‘‘design out” and eliminate the risks wherever possible. If this is
not possible it is the responsibility of the designer to minimise
the risks. When a contractor is appointed, the analysis of risks con-
tinues but now with the assistance of specialists in construction. A
Y. Zou et al. / Safety Science 97 (2017) 88–98 95
construction project is normally divided into a number of sub-
projects for managing risks at a sub-project level by considering
different activities and processes individually. Each sub-project
may have separate designers and contractors with their own risks
to identify and manage. A group of risk specialists (experts from
multi-disciplines) hired by the project team then need to collabo-
rate with project members to identify and investigate the potential
risks by interviews and discussions. A group of paper-based risk
documents (e.g. risk start-up report, risk inventory) are then com-
piled in this process. To implement risk management, specialists
who play facilitating roles during the risk management process
need to attend the project control meetings and keep tracking pro-
gress, and give advice on specific construction activities. However,
the project team, especially the managers, is required to be respon-
sible for the application of the risk management cycle. It is extre-
mely important to point out that many people will be involved
in the risk management during the lifecycle, so that any updated
risk information, decisions and changes should be recorded and
communicated effectively. Therefore, BIM-based risk management
is expected to facilitate efficient risk communication and support
the dynamic development process of a project.
6. Conclusion
Utilising BIM and BIM-related digital technologies to manage
risks has been a growing research interest in the AEC industry. Suc-
cessful use of these technologies requires a comprehensive under-
standing of the fundamentals, general process, techniques of risk
management and the relationship between the new and traditional
methods.
This paper summarises the current status and challenges of tra-
ditional risk management and has conducted a systematic and crit-
ical literature review on the state-of-the-art of BIM-based risk
management, and discussed the current obstacles and future
needs. The literature shows the implementation of traditional risk
management is still a manual undertaking, the assessment is heav-
ily reliant on experience and mathematical analysis, and the deci-
sion making is frequently based on knowledge and experience
based intuition, which leads to a decreased efficiency in the real
environment. To improve the above situation, some standards or
governmental documents (e.g. ISO 31010:2009, CDM regulations)
put emphasis on foreseeable risks being identified and mitigated
at an early stage and risk information should be documented and
updated during the development process of a project. This is where
BIM could be of help. BIM could not only be used as a systematic
risk management tool in the development process, but also act as
a core data generator and platform to allow other BIM-based tools
to carry out further risk analysis. The tools reviewed in this paper
include automatic rule checking, knowledge based systems, reac-
tive and proactive IT-based safety systems. The findings indicate
that most of the current efforts focus on investigating technical
developments and the management of construction personnel
safety risks is a main interest so far. Because BIM-based risk man-
agement is an emerging development, there are still some techni-
cal limitations and lack of ‘human factor’ testing in practice.
Therefore, these efforts are still at a conceptual or prototyping
stage and have not been broadly used in real workplaces. To over-
come this gap, we suggest future research should: (1) have a multi-
disciplinary system-thinking, (2) investigate implementation
methods and processes, (3) integrate traditional risk management
with new technologies, and (4) support the project development
process. In conclusion, though the area of BIM-based risk manage-
ment is just emerging and there is no ‘complete’ solution so far, the
area is important and will provide interesting opportunities in the
future.
Acknowledgement
This research is supported by University of Liverpool and China
Scholarship Council (CSC) financially (Grant number:
201408500090).
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    Knowledge management within construction project is strategic important to organizations for competition or best practices that leads to efficient and effective management of construction project. Construction organizations use various knowledge management strategies: experts data bases; cross construction project learning; active knowledge management; knowledge requests of experts; knowledge mapping; rewards; communities of practice; best practice transfer; competence management; expertapprentice relationship; groupware technologies; knowledge databases and book marking engines; intellectual capital; knowledge brokers; social e-network; storytelling; after construction project reviews; etc. Some of these strategies are part of various knowledge management models and theories. The purpose of this chapter is to present a model for integrated project management by undertaking a complex analysis of a construction project, organizational and external environment factors affecting it and to present recommendations on increasing its competitive ability. The chapter starts with basic definitions of project management, and then emphasizes the significance of knowledge management in construction projects. The second section presents a survey of the knowledge (explicit and tacit), knowledge management models, theories and systems. Tacit knowledge (expertise, understanding, skills, professional intuition, competence, experience, organizational culture, informal organizational communication networks, intellectual capital of an organization, ideals, traditions, values, and emotions) is properly described, respectively. This section describes the six-stage model for integrated construction project management. The level of efficiency of the integrated project management depends on the many projects' organizational and external variable factors and all these variable factors can be optimized. The main objective of this model is to analyze the best experiences in the field, to compare it and consequently to present particular recommendations. The third section describes the methods of multiple criteria evaluation for construction project management. Finally, the fourth section is devoted to the application of the model by studying the expertise of advanced industrial economies and by adapting it to Lithuania by taking into consideration its specific history, development level, needs and traditions.