Assessing environmental impact through Computational Structural Optimization

The urgent need to reduce the construction sector's environmental footprint is prompting a shift towards more sustainable methodologies. Traditional construction practices, marked by excessive resource consumption and high emissions, require immediate transformation. With their stringent energy and emission targets, European policies emphasize the critical role of integrated Life Cycle Assessment (LCA) in accurately evaluating sustainability. In this context, Structural Optimization (SO) techniques represent a powerful tool for incorporating environmental metrics within holistic design paradigms. This study presents a robust framework that synergistically integrates SO techniques with LCA methodologies to estimate and mitigate the environmental impacts of a space-frame structural system. Employing generative computational design techniques, this work leverages the Visual Programming Language (VPL) to define optimal configurations for parametric structural models. Specifically, the optimization process prioritizes material efficiency and Global Warming Potential (GWP) as key environmental metrics by simultaneously optimizing size, shape and topology. The implementation of a Multi-Objective Evolutionary Algorithm (MOEA) yields multiple solutions with the identification of a Pareto-optimal front, balancing structural performance with environmental considerations. This study demonstrates that the adoption of hybrid-material solutions incorporating timber elements can substantially reduce the associated GWP compared to traditional steel systems without penalizing structural efficiency. The results emphasize the importance of integrating environmental parameters within the conceptual design phase to promote sustainability in practical applications within the Architecture, Engineering and Construction (AEC) field.