An Intelligent Decision-Support Framework Based on Fuzzy BWM–TOPSIS with Interdependent Criteria for Alternative Selection in Complex Construction Projects

This study proposes an intelligent decision-support framework for alternative selection in complex construction projects, where evaluation processes are affected by uncertainty, multiple decision-makers, and interdependent criteria. The framework integrates the fuzzy group best–worst method with fuzzy TOPSIS into a unified structure that explicitly captures cross-criterion influence effects. First, triangular fuzzy judgments from multiple experts are used to derive criterion weights, while interdependencies among criteria are represented through a fuzzy influence-intensity matrix and incorporated into fuzzy nonlinear optimization models. This process enables the systematic estimation of both independent and interdependency-adjusted criterion weights. Second, the resulting weights are used in a fuzzy ranking procedure to evaluate alternatives according to their relative closeness to fuzzy ideal solutions. To enhance transparency, reproducibility, and practical usability, the proposed method is implemented in Python as an automated computational workflow for decision analysis. Its applicability is demonstrated through a real-world case study on access platform system selection for mechanical, electrical, and plumbing installation in an airport terminal subject to safety, productivity, workspace, and elevation-related constraints. The results show that explicitly modeling criterion interdependencies provides a more realistic evaluation structure and enhances the robustness and reliability of alternative selection in complex construction management contexts.