Assessing and Prioritizing Construction Contracting Risks with an Extended FMEA Decision-Making Model in Uncertain Environments

Mahdieh Mohammadi; Sara Sarvi; Saied Jafarzadeh Ghoushchi

doi:10.31181/sdmap31202642

Authors

Mahdieh Mohammadi Department of Quantity Surveying, Faculty of Built Environment, University of Malaya, Kuala Lumpur, Malaysia Author https://orcid.org/0000-0002-4987-0387
Sara Sarvi Faculty of Industrial Engineering, Urmia University of Technology, Urmia, Iran Author https://orcid.org/0009-0007-2759-4442
Saied Jafarzadeh Ghoushchi Faculty of Industrial Engineering, Urmia University of Technology, Urmia, Iran Author https://orcid.org/0000-0003-3665-9010

DOI:

https://doi.org/10.31181/sdmap31202642

Keywords:

MABAC, SWARA, FMEA, Uncertainty, MCDM, Fuzzy sets, Decision Making

Abstract

This study aims to enhance risk assessment in construction contracting by addressing the limitations of traditional Failure Modes and Effects Analysis (FMEA), particularly its inability to effectively handle uncertainty and prioritize risks with equal Risk Priority Numbers (RPNs). To overcome these issues, a hybrid decision-making framework was developed by integrating fuzzy logic with the Step-wise Weight Assessment Ratio Analysis (SWARA) and Multi-Attributive Border Approximation Area Comparison (MABAC) methods. The proposed model enables more accurate weighting of risk criteria and prioritization of risks under uncertain conditions. A case study involving 18 identified construction contracting risks demonstrated the model’s effectiveness, with key risks such as inadequate technical skills and poor contract conditions ranked highest due to their severe impact on cost and project outcomes. The hybrid approach improved the clarity and consistency of risk prioritization compared to traditional methods. This research contributes a flexible and practical tool for managing risks in complex construction environments. Future studies are encouraged to explore integration with real-time project monitoring systems and the application of this model across different infrastructure domains.

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