2018
DOI: 10.3390/sym11010012
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Risk Level Evaluation on Construction Project Lifecycle Using Fuzzy Comprehensive Evaluation and TOPSIS

Abstract: A risk is a predominant problem in the developing countries construction projects. Although numerous studies have been concerned on risk, there is a limited study on a mechanism to identify the typical risks and effects level. This paper presents an approach for evaluating the risks in case of schedule delays at the various lifecycles of construction projects. The methodology applied is an integrated model of the technique for order preference by similarity to ideal solution (TOPSIS) and fuzzy comprehensive ev… Show more

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Cited by 37 publications
(25 citation statements)
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“…Comprehensive Evaluation and TOPSIS (Gebrehiwet & Luo, 2019). Implementation of AHP and TOPSIS Method to Determine the Priority of Improving the Management of Government's Assets (Febriansyah, et al, 2017 (Walaszczyk, 2016).…”
Section: Risk Level Evaluation On Construction Project Lifecycle Using Fuzzymentioning
confidence: 99%
See 2 more Smart Citations
“…Comprehensive Evaluation and TOPSIS (Gebrehiwet & Luo, 2019). Implementation of AHP and TOPSIS Method to Determine the Priority of Improving the Management of Government's Assets (Febriansyah, et al, 2017 (Walaszczyk, 2016).…”
Section: Risk Level Evaluation On Construction Project Lifecycle Using Fuzzymentioning
confidence: 99%
“…Risk management is a process of identifying, analyzing, assessing, controlling, and an effort in avoiding, minimizing, or even eliminating unacceptable risks (Gebrehiwet & Luo, 2019). In companies, risk management is a process of planning, organizing, directing, and controlling the activities of an organization to minimize the risk of company revenue (Purohit, et al, 2018).…”
Section: Risk Managementmentioning
confidence: 99%
See 1 more Smart Citation
“…In 1975, Nuclear Regulatory Commission (NRC) developed and established the famous probabilistic risk assessment method, which became a milestone in the history of engineering risk analysis. Misra et al proposed a fuzzy fault tree analysis method, which was applied in safety risk assessment [16]; Dunyak et al established a reliable fuzzy model by introducing the expansion theory, which can effectively deal with the fuzziness of the risk of each bottom event in the fault tree [17]; Roberts proposed the integrated quantitative risk management theory and established the decision support theory after risk analysis, arranging and screening [18]; Fung et al created a risk assessment model, which can help identify and predict the risk levels [19]; Leu et al built a safety risk assessment model based on improved Bayesian network (BN) of fault tree (FT) [20]; Gamble et al established the fuzzy and grey correlation analysis model and evaluated duration risk, cost risk, quality risk and safety risk separately [21]; Aminbakhsh et al presented a safety risk assessment framework, which can give priority to the security risk of the project before making a reasonable budget [22]; Tian proposed a grey hierarchy evaluation model, which can assign the importance priority sequence of safety management measures [23]; Zhao et al proposed a a hybrid risk evaluation model based on the cloud model and fuzzy comprehensive evaluation method [24]; Esmaeili et al presented an attribute-based risk identification and analysis method [25,26]; Ning et al established a safety risk assessment model to carry out site layout and safety management [27]; Huang et al proposed an improved AHP-Grey model of construction safety evaluation, this model can better reflect the actual safety situation of construction [28]; Gebrehiwet et al proposed an integrated model of the technique for order preference by similarity to an ideal solution (TOPSIS) and fuzzy comprehensive evaluation (FCE) [29]; Leśniak et al presented a method for creating risk management strategies using a standard algorithm that includes risk identification, risk analysis, and risk assessment [30]; Dong et al proposed the Pb-Zn mine safety risk evaluation model based on the fuzzy-grey correlation analysis method [31].…”
Section: Literature Reviewmentioning
confidence: 99%
“…It is assumed that the single index fuzzy evaluation is the first level fuzzy comprehensive evaluation, and the comprehensive evaluation matrix of second level fuzzy comprehensive evaluation is the vector composition matrix of the results of each level fuzzy comprehensive evaluation [36,37].…”
Section: Multi-level Fuzzy Comprehensive Evaluationmentioning
confidence: 99%