Multi-Objective Bayesian Optimization-Driven AI Framework for Material Strength Enhancement and Carbon-Waste Minimization in Sustainable Infrastructure
Keywords:
Multi-objective Bayesian optimization; Sustainable infrastructure; Material optimization; Gaussian process regression; Embodied carbon; Waste minimization; Pareto optimization.Abstract
Sustainable infrastructure materials design involves balancing multiple goals of performance and impacts; mechanical properties with embodied carbon and material-waste reduction.The study suggests a Multi-Objective Bayesian Optimization (MOBO) approach to optimize the following five material design variables: cement/binder contents, water-to-binder ratio, replacement of supplementary cementitious material, replacement of recycled aggregate, and addition of waste-derived material.The synthetic computational responses are modeled with Gaussian Process Regression (GPR) and the Expected Hypervolume Improvement (EHVI) is used for sequential selection towards Pareto-efficient solutions.The framework is contrasted with the Random Search and Single-Objective Bayesian Optimization strategies, as well as NSGA-II with a shared budget setting of 100 evaluations.The chosen MOBO compromise solution obtains a material-waste burden index of 57.50%, ????????₂????/????³ embodied carbon of 224.00 kg, and a compressive strength of 58.70 MPa.The solution offers a 32.06% improvement in strength, a 25.40% carbon reduction and a 27.22% material-waste burden reduction when compared to the baseline design of 44.45 ????????????, 300.25 kg CO₂e/m³, and 79.00% material-waste burden.MOBO has a normalized hyper volume of 0.874.These computational result
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