Prediction-to-Prescription Framework for Sustainable Compression-Cast Concrete Using Machine Learning and Multiobjective Optimization

Abstract

The novel compression-cast concrete (CCC) delivers superior mechanical and durability performance over conventional vibration-cast concrete (VCC), alongside economic and environmental advantages. However, its widespread adoption requires an optimized and systematic design method. This study presents a data-driven framework that integrates machine learning (ML) and multiobjective optimization for both forward prediction and inverse design of CCC. Using an experimental data set, various ML models were trained, with Optuna-optimized backpropagation neural networks (OP_BPNN) showing the best accuracy. Model interpretability was enhanced using individual conditional expectation and Shapley additive explanations. The validated OP_BPNN served as a surrogate in inverse optimization via nondominated sorting genetic algorithm III (NSGA-III), targeting compressive strength while minimizing cost and CO 2 emissions and maximizing density. Optimal solutions were ranked using the technique for order of preference by similarity to ideal solution (TOPSIS). Compared to VCC, the optimized CCC showed up to 15% potential reduction in cost and 38% lower CO 2 emissions, as predicted by the model within the studied parameter range. A user-friendly graphical interface was developed to facilitate practical implementation. The framework offers a scalable tool for CCC design aligned with project-specific performance and sustainability goals.