A multi-objective optimization design framework for ultra-high performance concrete based on stacking ensemble learning

Abstract

Ultra-high performance concrete (UHPC), an advanced cementitious composite characterized by superior mechanical properties and durability, requires multi-objective collaborative optimization in mix proportion design to facilitate its large-scale engineering applications. To address this challenge, this study proposes an intelligent design framework for UHPC driven by the synergistic integration of Stacking ensemble learning and the non-dominated sorting genetic algorithm III (NSGA-III). In the model construction stage, a Stacking ensemble model was developed by integrating eight heterogeneous algorithms as base learners and employing linear regression as the meta-learner. Subsequently, six hyperparameter optimization strategies were employed to fine-tune the model. At the optimization decision stage, the optimal Stacking ensemble model was embedded into the NSGA-III algorithm, and the technique for order preference by similarity to ideal solution (TOPSIS) was employed to select the optimal mix design from the Pareto front. The results demonstrate that the predictive accuracy of the Stacking ensemble model significantly outperforms that of the individual single-algorithm models. Furthermore, multi-objective optimization conducted for three target strength grades, UC100, UC120, and UC140, revealed that all optimized schemes successfully meet the specified strength requirements while effectively balancing environmental impact and production cost. This research establishes a data-driven, multi-criteria intelligent decision-making pathway for the multi-objective design of UHPC, with substantial implications for advancing the interdisciplinary integration of materials science and artificial intelligence.