Journals Proceedings

Abstract

Contracting for construction services is an inherently risky venture for the owner, design agent and contractor. All of these parties are exposed to unanticipated risks, exposure to economic loss and unforeseen contract liability while performing under the contract. Project risk management, therefore, has been recognised critical for the construction industry to improve their performance and secure the success of projects. Risk assessment is the most important step in risk management. Classical methods for risk assessment are no longer accurate and effective, therefore, many papers introduced fuzzy logic as a more accurate and effective technique in risk assessment. In this paper, a comparison between two fuzzy risk assessment methods; Nieto-Morote and Ruz-Vila [1] and Kuo and Lu [2] is done using the same input parameters which are risk probability (RP), risk impact (RI) and risk discrimination (RD) to determine if these methods give the same risk ranking or not. Actually, the comparison results in different risk ranking, because the Nieto-Morote and Ruz-Vila [1] method depends on minimization error tool to minimize inconsistency in results, and this tool always doesn't give optimum results, while, we can consider Kuo and Lu [2] method more accurate because it depends on eliminating the inconsistency in results using a transformation process step to remain the decision matrix with reciprocity and additive consistency.