Application of Fuzzy Logic for Optimizing Resource Allocation in Complex Construction Environments

Abstract

The rapid growth of construction projects has intensified inefficiencies in resource allocation, leading to cost overruns, delays, and reduced quality. This study proposes an integrated fuzzy logic framework to optimize resource allocation under complex and uncertain construction conditions. The framework combines fuzzy information quantification, fuzzy clustering, multi-objective decision-making, and adaptive control modules into a coherent system. A dataset covering residential, commercial, and infrastructure projects was used to evaluate the model against linear programming, dynamic programming, neural networks, and genetic algorithms. Results show that the proposed model achieves a resource waste rate of 7.5% compared with 19.5% for linear programming, and a faster allocation response speed of 0.775. Under complex geological conditions, the configuration effect reaches 88.33%, and in severe weather scenarios it maintains 86% effectiveness, both outperforming competing models. These findings highlight the model’s computational efficiency, scalability across project scales, and adaptability to uncertain environments, offering a robust approach for sustainable construction management