Measuring the Construction Risk Insurability through Fuzzy Inference System - Leonardo Yonatan Tan; Andreas Wibowo; Andreas A. Pramudya (Institute of Research and Community Outreach - Petra Christian University, 2024)
Contractors face most of the construction risks among stakeholders, and insurance is a common method to mitigate these risks. However, not all risks are insurable. While prior studies have typically assessed risk insurability through a binary approach (insurable versus non-insurable) and lacked clear criteria, this study offers a novel perspective by evaluating the insurability of construction risks based on four criteria: ‘accidental events,’ ‘quantifiable,’ ‘numerous and homogenous,’ and ‘evaluable.’ This study develops a fuzzy-based model to assess the degree of the construction risk insurability, accounting for the uncertainty, imprecision, and vagueness inherent in evaluating insurability against a specific criterion and criteria combinations. The model is applied to assess the insurability of several construction risks, illustrating its practical application. This paper concludes by discussing the model’s limitations and suggesting directions for future research.