A proposal for construction project risk assessment using fuzzy logic

Construction Management and Economics (Impact Factor: 0.8). 02/2000; 18(4):491-500. DOI: 10.1080/01446190050024905
Source: RePEc


The construction industry is plagued by risk and often has suffered poor performance as a result. There are a number of risk management techniques available to help alleviate this, but usually these are based on operational research techniques developed in the 1960s, and for the most part have failed to meet the needs of project managers. In this paper, a hierarchical risk breakdown structure representation is used to develop a formal model for qualitative risk assessment. A common language for describing risks is presented which includes terms for quantifying likelihoods and impacts so as to achieve consistent quantification. The relationships between risk factors, risks and their consequences are represented on cause and effect diagrams. These diagrams and the concepts of fuzzy association and fuzzy composition are applied to identify relationships between risk sources and the consequences for project performance measures. A methodology for evaluating the risk exposure, considering the consequences in terms of time, cost, quality, and safety performance measures of a project based on fuzzy estimates of the risk components is presented.

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    • "As for project delay, Shen [7] presented, based on a questionnaire, eight major risks. By using the hierarchical risk breakdown structure (HRBS), Tah and Carr [8] identified the internal and external risks in construction industry. Shen et al. [9] listed six groups of risks: financial, legal, management, market, policy and political. "

    The 13th Faculty of Engineering and Architecture Student and Alumni Conference at the American University of Beirut, Beirut, Lebanon; 05/2014
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    • "Even though 'human' experts can often accomplish a reasonable project result, deficits almost always follow due to managers failing to take all relevant factors into consideration and/or lacking access to all relevant information (Cheng, Tsai, & Sudjono, 2012; Cheng et al., 2009). The construction industry in the past has been plagued with similar problems and is categorised by (a) specific intricacy variables due to individual industry ambiguities and inter-dependencies , and (b) insufficiency of operations (Beavers et al., 2006; Dubois & Gadde, 2002; Tah & Carr, 2000). Despite the availability of several intelligent systems, construction managers are still perplexed when faced with a new problem in decision-making, when they ought to establish which existing intelligent systems are most apposite given the nature of the system, the objectives for development , time constraints and computing capacity (Bisaillon, Cordeau, Paporte, & Pasin, 2011; Bolduc, Renaud, Boctor, & Paporte, 2008; Cui & Lu, 2009). "
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    ABSTRACT: With the increasing complexity of problems in the construction industry, researchers are investigating computationally rigorous intelligent systems with the aim of seeking intelligent solutions. The purpose of this paper is therefore to analyse the research published on ‘intelligent systems in the construction industry’ over the past two decades. This is achieved to observe and understand the historical trends and current patterns in the use of different types of intelligent systems and to exhibit potential directions of further research. Thus, to trace the applications of intelligent systems to research in the construction industry, a profiling approach is employed to analyse 514 publications extracted from the Scopus database. The prime value and uniqueness of this paper lies in analysing and compiling the existing published material by examining variables (such as yearly publications, geographic location of each publication, etc.). This has been achieved by synthesising existing publications using 14 keywords2 ‘Intelligent Systems’, ‘Artificial Intelligence’, ‘Expert Systems’, ‘Fuzzy Systems’, ‘Genetic Algorithms’, ‘Knowledge-Based Systems’, ‘Neural Networks’, ‘Context Aware Applications’, ‘Embedded Systems’, ‘Human–Machine Interface’, ‘Sensing and Multiple Sensor Fusion’, ‘Ubiquitous and Physical Computing’, ‘Case-based Reasoning’ and ‘Construction Industry’. The prime contributions of this research are identified by associating (a) yearly publication and geographic location, (b) yearly publication and the type of intelligent systems employed/discussed, (c) geographic location and the type of research methods employed, and (d) geographic location and the types of intelligent systems employed. These contributions provide a comparison between the two decades and offer insights into the trends in using different intelligent systems types in the construction industry. The analysis presented in this paper has identified intelligent systems studies that have contributed to the development and accumulation of intellectual wealth to the intelligent systems area in the construction industry. This research has implications for researchers, journal editors, practitioners, universities and research institutions. Moreover, it is likely to form the basis and motivation for profiling other database resources and specific types of intelligent systems journals in this area.
    Expert Systems with Applications 03/2014; 41(4):934–950. DOI:10.1016/j.eswa.2013.06.061 · 2.24 Impact Factor
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    • "Among these elements, risk measurement/ assessment is the most difficult task, which involves evaluation of the probability of occurrence of risk events and their impact on the project (Thomas et al., 2006). For international construction projects, past research studies have proposed various approaches for project risk assessment/measurement, including: decision analysis (Jeljeli and Russell, 1995; Mulholland and Christian, 1999; Han and Diekmann, 2001), fuzzy set analysis (Jablonowski, 1994; Tah and Carr, 2000), simulation and sensitivity analysis (Woodward, 1995; Songer et al., 1997; Ye and Tiong, 2000), and analytical hierarchy process analysis (Mustafa and Al-Bahar, 1991; Hastak and Shaked, 2000). In most of the infrastructure BOT projects, simulation and sensitivity analysis are used for technical and financial risk assessment; however, risk measurement is often constrained by the non-availability of past information on the impact of important risk factors, including events which affect the risk profile of BOT projects (Thomas et al., 2006). "
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    ABSTRACT: With the growing strains on public resources, many governments in recent years have turned to the private sector for infrastructure project financing. The special purpose vehicles (SPVs) taking on such projects usually have a two-stage business model: a construction stage followed by an operating stage. However, the project risk in stage 1 is very high, and in most cases, the impacts of specific construction events on project risk and capital cost are unobservable owing to lack of informational transparency. Eurotunnel (the Channel Tunnel project) is unique in that the share price data for the entire construction period are publicly available. Based on event study methodology, empirical tests were conducted for several well-documented Eurotunnel construction events to measure and assess the project risk and the impacts of such events on the SPV’s equity value. The test results show that: (1) during the construction stage, efforts to better manage the interests and incentives of contractors produce more significant positive impact on investors than efforts for cost containment; (2) during the construction stage, meeting the project deadline is a higher investor priority than containing construction cost; and (3) once the construction phase is complete, the investors’ priority then becomes the overall cost and the impact of construction events on the expected returns from investment. Finally, the level of risk and the potential conflicts of interest that arise during the construction phase of a mega infrastructure project are such that turning to IPOs to provide equity capital may not be appropriate.
    Construction Management and Economics 03/2013; 31(3). DOI:10.1080/01446193.2012.761715 · 0.80 Impact Factor
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