Construction-project risk assessment by a new decision model based on De-Novo multi-approaches analysis and hesitant fuzzy sets under uncertainty

• Published in: Journal of intelligent & fuzzy systems Vol. 35; no. 1; pp. 639 - 649
• Main Authors: Zolfaghari, S., Mousavi, S. Meysam
• Format: Journal Article
• Language: English
• Published: Amsterdam IOS Press BV 01.01.2018
• Subjects: Decision analysis; Decision making; Failure analysis; Fuzzy sets; Project management; Ranking; Risk assessment; Risk management
• ISSN: 1064-1246; 1875-8967
• DOI: 10.3233/JIFS-162013

Project risk management is an essential activity that refers to identify and analyze potential risks to take precautionary responses to mitigate their negative impacts or intensify their positive effects. This study presents a novel approach of failure mode and effect analysis (FMEA) by the aim of prioritizing construction project risks under uncertain conditions of projects. The examined data are defined in the form of a new extension of fuzzy sets, namely hesitant fuzzy sets (HFSs), to involve more possible memberships for evaluation risks’ characteristics. Then, the application of H-VIKOR, VIKOR under hesitant information, and H-Shapley VIKOR is demonstrated to determine ranking order of construction-project risks. A new version of H-Shapley VIKOR method is presented in this study by defining a new evaluation index for ranking alternatives. Moreover, using different multi-attributes decision-making (MADM) methods may lead to several ranking orders. Rank reversal makes problem in complex decision-making process of the construction projects. To solve this problem, a new extension of De-Novo multi-approaches multi-attributes analysis by a group of experts or decision makers (DMs) is introduced to determine precise final ranking order. The extended method is encompassed the preference of project DMs about using different MADM methods. The application and performance of the proposed new FMEA model-based hesitant fuzzy De-Novo multi-approaches-multiple attributes analysis are evaluated through a real case study of highway project risks. The results indicate the efficiency of the proposed FMEA model.