An intelligent multi-attribute decision-making method for construction risk assessment using hypersoft rough pythagorean fuzzy settings

Abstract

Risk assessment is crucial in construction project management, as uncertainties like budget fluctuations, resource availability, equipment failures, and safety risks can impact project outcomes. Traditional methods may not fully capture these uncertainties. This study proposes an intelligent multi-attribute decisionmaking (MADM) methodology based on hypersoft rough Pythagorean fuzzy (HSRPF) sets to address these challenges. The HSRPF model extends Pythagorean fuzzy sets (PyFS) by integrating rough set (ROS) and hypersoft sets (HYPSS) theory, allowing for a more comprehensive representation of vagueness. The lower approximation is employed to depict risks that can ultimately be categorized based on available information. Conversely, the upper approximation describes risks for which there is insufficient information to make a professional judgement. The primary objective of this study is to develop an HSRPF-based risk assessment framework tailored for construction projects by merging lower and upper approximations to model uncertainty in expert-driven risk assessments. The study also aims to introduce a new multi-attribute decision-making method (MADM) to systematically rank and prioritize risks, ensuring efficient resource utilization and effective risk mitigation strategies. The study validates a proposed approach for construction risk management by examining highway project risks, enhancing decision-making and empowering project managers to navigate complex environments with confidence.