An integrated risk and productivity assessment model for public–private partnership projects using fuzzy inference system

Abstract

Infrastructural projects based on public–private partnership (PPP) schemes usually struggle with large amounts of resources. Given such projects’ relatively long lifetime, it is expected to encounter a wide range of risks when executing them. This implies that increasing productivity based on cost, time, and quality goals requires accurately identifying and assessing risks in these projects. This study aims at risk assessment and analysing productivity in PPP projects based on an integrated analysis method. To this end, first, library studies are utilized to identify the most significant risks by surveying the PPP-based urban underground construction projects. Expert opinions and the fuzzy Delphi (FD) technique are then employed in three stages to identify 48 secondary risk factors. By identifying the sources of these secondary risk factors, we identify five primary risk dimensions: government, financing, operation, constructability, and organization-related risks. A questionnaire is distributed to acquire experts’ opinions and screen the most critical risks by integrating FD with fuzzy failure mode and effects analysis (FFMEA). Results showed that, in total, 23 secondary risk factors in the five primary risk dimensions exhibited risk priority number (RPNNormalized) values above 0.5, indicating their relatively more critical impacts. In the next phase, a multi-stage fuzzy inference system is designed to evaluate the impacts of critical risks on the productivity of PPP projects. Output results of the inference system indicated very high, high, and medium impacts of the risks on the overall productivity of the PPP projects in terms of time, cost, and quality, respectively.