A novel framework for developing environmentally sustainable and cost-effective ultra-high-performance concrete (UHPC) using advanced machine learning and multi-objective optimization techniques

This study aims to propose a novel framework for strength prediction and multi-objective optimization (MOO) of economical and environmentally sustainable ultra-high-performance concrete (UHPC) which aids in intelligent, sustainable, and resilient construction. Different tree- and boosting ensemble-based machine learning (ML) models are integrated to form an accurate and reliable prediction model for the uniaxial compressive strength of UHPC. The optimized models are integrated into a super learner model, resulting in a robust predictive model that is used as one of the objective functions in the MOO problem. A total of 19 objective functions are considered, including cost, uniaxial compressive strength, and 17 environmental impact categories that comprehensively evaluate the environmental sustainability of the UHPC mix. The resulting impacts from the mid-point indicators were calculated using the Eco-invent v3.7 Life Cycle Inventory database. The results showed that the super learner model accurately predicted the uniaxial compressive strength of UHPC. The MOO resulted in Pareto fronts, demonstrating the trade-off among the uniaxial compressive strength, cost, and environmental sustainability of the mix and a broad range of solutions that can be obtained for the 19 objectives. The study provides a useful tool for designers and decision-makers to select the optimal UHPC mixture that meets specific project requirements. Finally, for the practical application of the ML predictive model and MOO algorithm for UHPC, a graphical user interface-based software tool, FAI-OSUSCONCRET, was developed. This software tool offers fast, accurate, and intelligent predictions and multi-objective optimizations tailored to specific project requirements, thus resulting in a UHPC mixture that perfectly meets project needs.