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

ABSTRACT 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.

  • [Show abstract] [Hide abstract]
    ABSTRACT: This paper presents a fuzzy risk assessment model for construction projects. The model combines the fuzzy weighted average principle with a similarity measure of generalized fuzzy numbers. The failure probability of each project objective can be evaluated using a discrete fuzzy weighted average algorithm and translated into an appropriate fuzzy linguistic term by using a modified similarity measure determined by considering the area, perimeter, height, and geometric distance of generalized fuzzy numbers. This paper makes practical contributions by suggesting a model that can address the uncertainty associated with construction projects based on fuzzy set theory and facilitate the assessment of fuzzy risks by allowing for sophisticated computations and theoretical contributions by enabling researchers to expeditiously assess project risks. A test case verifies the usability and validity of the proposed method.
    KSCE Journal of Civil Engineering 03/2014; 18(2):521-530. DOI:10.1007/s12205-014-0053-x · 0.51 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Managers require a good understanding about the nature of risks involved in a construction project because the duration, quality, and budget of projects can be affected by these risks. Thus, the identification of risks and the determination of their priorities in every phase of the construction can assist project managers in planning and taking proper actions against those risks. Therefore, prioritizing risks via the risk factors can increase the reliability of success. In this research, first the risks involved in construction projects has been identified and arranged in a systematic hierarchical structure. Next, based on the obtained data an Adaptive Neuro-Fuzzy Inference System (ANFIS) has been designed for the evaluation of project risks. In addition, a stepwise regression model has also been designed and its results are compared with the results of ANFIS. The results show that the ANFIS models are more satisfactory in the assessment of construction projects risks. Our proposed methodology can be applied by managers of construction projects and practitioners to assess of risk factor of construction projects in a proper manner.
    KSCE Journal of Civil Engineering 06/2014; 18(5):1213-1227. DOI:10.1007/s12205-014-0139-5 · 0.51 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Due to their complexity, construction projects involve significant risks that must be managed in order to meet the main project objectives in terms of cost, time and quality. While some risks can be foreseen at the beginning of a project and allocated among the project actors, other risks are difficult to predict. Moreover, even identified risks may change in scope and require different types of response. In order to manage such risks successfully, collaborative efforts among project actors are needed. This thesis focuses on collaborative management of risks in construction projects – joint risk management (JRM) – which is claimed to provide several advantages in comparison to separate risk management by each project actor. An overall aim is to increase the understanding of how JRM can be enhanced throughout a project’s lifecycle. The underlying studies this thesis is based upon constitute a multiple case study of nine construction projects, a questionnaire survey and a longitudinal case study of three construction projects. Empirical data were collected through interviews, observations of JRM workshops and document studies. The empirical findings show that cooperative procurement procedures, organic management systems and appropriate strategies for addressing agency-related problems enhance JRM in construction projects. Thus they require thorough consideration when organizations intend to implement JRM. This thesis provides several contributions to risk management theory. Firstly, the author extends the definition of JRM by including its core components together with associated activities and underlying factors. The extended definition better reflects, and increases understanding of, the nature of JRM. Secondly, the research contributes to discussion of serious drawbacks related to traditional procurement practices by identifying and studying procurement variables (project delivery method, form of payment and use of collaboration or partnering arrangements) that have a major influence on risk management. In addition, the results of questionnaire survey suggest that cooperative procurement procedures in general and collaborative activities in particular are positively related to the use of JRM. Finally, by framing the empirical results in an organizational theory context this research identifies two sets of factors that strongly influence the implementation and effectiveness of JRM, related to management system (organic vs. mechanistic), and strategy for responding to agency-related problems. By applying theory on mechanistic and organic organization to RM, the study pinpoints the importance of managing tensions between control and flexibility when implementing JRM. The author suggests that JRM requires a combination of formal tools (aimed at controlling identified risks) and flexible strategies (aimed at responding to unforeseen events). By investigating how strategies to handle agency-related problems can foster collaborative relationships and JRM, this research contributes to RM literature where few studies have discussed JRM from the perspective of the principal – agent relationships.
    02/2014, Degree: PhD in Construction Engineering