Fuzzy Consensus Qualitative Risk Analysis Framework for Building Construction Projects

Abstract

This paper presents a Fuzzy Consensus Qualitative Risk Analysis Framework to identify and prioritize risks encountered in Building Construction Projects, which is applied to developing countries. The framework utilizes and compares three fuzzy consensus aggregation algorithms to select the most appropriate one in aggregating experts' opinions in prioritizing risks: (1) Fuzzy Similarity Aggregation Method (FSAM); (2) Fuzzy Distance Measurement Method (FDMM); and (3) Fuzzy Optimal Aggregation Method (FOAM). Risks are identified through literature review and interviews with experts who rate the risks in three dimensions (probability of occurrence, impact, and level of detection). Five linguistic terms, which are defined by five trapezoidal membership functions (MFs), are used to collect experts' opinions, using a 5-point linguistic rating scale. Expert opinions are aggregated in a linguistic framework using the three algorithms to compare their results and ensure that the aggregated decision is a result of their common agreement. The Euclidean Distance Measure Function is applied to the results of each algorithm to determine risk criticality. In order to select the most appropriate method to aggregate experts' opinions, a three-step Delphi technique is utilized to identify the criticality of similar risks that were encountered in actual Building Construction Projects. The results of this stage are compared to the output results of each of the three algorithms to decide on the method that best matches the results of actual projects and risks are prioritized accordingly. The framework solves a major problem for construction project teams in developing countries by conducting qualitative risk analysis in a fully supported linguistic environment, which addresses the vagueness and imprecision that exist in the decision-making process. It also determines the best aggregation method to solve the problem, using a case study in Egypt.