Impact of Water Consumption on Structural Members in RC Frames Using Multi-Objective Metaheuristics Algorithm

Abstract

The construction industry has a significant global water footprint because buildings incorporate large quantities of embedded materials, such as concrete and steel, whose production consumes substantial amounts of water throughout their life cycle. The grey wolf optimizer (GWO) is particularly suitable for this problem because it is a population-based metaheuristic with strong exploration and exploitation balance, which makes it effective in navigating large discrete search spaces such as structural design variables. GWO has demonstrated robustness in multi-objective problems by efficiently approximating Pareto fronts and avoiding local optima. A numerical structural analysis and design model was developed via an application programming interface. This study is indeed the first optimization of the weight of reinforced concrete (RC) structural elements considering virtual water, with the given structural specifications and using the proposed multi-objective metaheuristic methodology. Results showed the optimal structural weight to be 266 tons, with virtual water usage reaching approximately 253 m3. These findings provide actionable insights for sustainable structural design, guiding material selection and early-stage decision-making to minimize virtual water consumption in RC buildings. This study addresses the research gap by introducing virtual water, alongside structural weight, as a novel objective function within multi-objective metaheuristic optimization of RC frames.