Prefabricated construction offers numerous benefits such as efficiency and sustainability but faces challenges in risk evaluation. A novel method combining TOPSIS, prospect theory, and interval-valued Pythagorean fuzzy numbers (IVPFNs) has been developed to address these challenges. This approach enhances the accuracy of risk assessment by incorporating decision-makers' psychological states and providing a structured framework for evaluating risks in prefabricated building projects.

Advancements in Risk Evaluation Methodology

The proposed methodology integrates advanced techniques to evaluate construction risks more comprehensively. By leveraging IVPFNs, it captures expert opinions with greater precision compared to traditional methods. Additionally, the inclusion of prospect theory ensures that the subjective preferences of decision-makers are thoroughly considered, leading to a more realistic simulation of the decision-making environment.

This innovative model addresses the limitations of existing evaluation methods. Traditional models often overlook the influence of managerial attitudes on risk outcomes. The new model rectifies this by integrating prospect theory, which considers how managers perceive gains and losses. Furthermore, the use of IVPFNs allows for a nuanced representation of expert opinions, enhancing the overall robustness of the risk assessment process.

Application and Validation of the New Model

The practical application of the proposed model is demonstrated through a real-world case study of a university teaching building project in Shanghai. The study involves inviting experts to evaluate various construction plans using linguistic terms converted into IVPFNs. The results indicate that the model effectively ranks alternatives based on their benefit-loss ratios, providing valuable insights for project management.

To validate the effectiveness of the proposed method, it was compared with other established models like PT-IVHFS-TOPSIS, PT-VIKOR, and SVN-PT-TOPSIS. The comparative analysis reveals that while there are differences in rankings, the optimal solution remains consistent across methods. This consistency underscores the reliability and feasibility of the new model. Moreover, the comparison highlights the shortcomings of existing methods, such as inadequate capturing of expert attitudes and hesitation, reinforcing the advantages of the integrated approach.