Machine Learning–Driven Optimization of Ultrahigh-Performance Concrete: Single-Objective and Multiobjective Approaches

Abstract

Ultrahigh-performance concrete (UHPC) offers superior mechanical properties and durability but is constrained by high density, cost, and environmental impact due to its cement-intensive composition. This study presents a machine learning (ML)–driven multiobjective optimization framework for UHPC mix design, integrating waste aggregates, supplementary cementitious materials (SCMs), and performance-enhancing components to balance strength and sustainability. A robust data set combining 694 literature-derived and 87 experimental data points underpins the framework. Unsupervised anomaly detection (isolation forest) is employed to refine data quality, while ML techniques identify key parameters influencing UHPC properties. Predictive models, including artificial neural networks, random forests, and light gradient-boosting machine (LightGBM), are trained on full and reduced feature sets to ensure accuracy and generalizability. LightGBM, showing the best predictive performance, is embedded into single-objective and multiobjective optimization processes. Single-objective optimization achieves rapid and accurate convergence for individual performance targets. Multiobjective optimization yields diverse Pareto-optimal solutions, exposing trade-offs among strength, cost, and embodied carbon. Experimental validation confirms that optimized mixes improve performance while reducing environmental footprint. Two novel UHPC mixes were experimentally validated: (1) an SCM-based formulation that reduces clinker use while maintaining structural integrity; and (2) a UHPC mix incorporating waste glass aggregates, enhancing circularity and lowering carbon emissions. This study provides a scalable, data-driven approach to ecoefficient UHPC design, enabling intelligent, application-ready solutions that align with construction performance demands and global sustainability goals.