Design of aerogel-incorporated concrete based on machine learning and multi-objective optimization

This study employs machine learning (ML) techniques to investigate the compressive strength, flexural strength, and thermal conductivity of aerogel-incorporated concrete (AIC). A database comprising 388 sets of experimental parameters and corresponding performance results was established and applied to train and evaluate six supervised ML algorithms. The results demonstrate that the Light Gradient Boosting Machine (LightGBM) achieved the best predictive performance for compressive strength and thermal conductivity, whereas the Extreme Gradient Boosting (XGBoost) performed best for flexural strength. All optimal models achieved coefficients of determination above 0.95 on the test set. To improve model transparency, the Shapley Additive Explanations analysis was introduced to interpret the optimal models. Parameter analysis was further conducted to reveal the relative contribution and directional effect of each input feature on the predicted properties. The results indicate that aerogel content and water-to-binder ratio are the dominant factors governing the three performance indicators, whereas the effects of mineral admixture content, curing age, and testing condition vary across different target properties. Based on these findings, design recommendations were proposed to enhance compressive strength, improve flexural strength, and reduce thermal conductivity, thereby providing a data-driven basis for the mix design of AIC. Furthermore, an interactive software platform integrating prediction and interpretability functions was developed to enable rapid performance assessment. Finally, a multi-objective optimization framework was developed to simultaneously maximize compressive strength and flexural strength and minimize thermal conductivity. The resulting trade-off solutions were systematically evaluated to identify representative mix designs corresponding to different performance priorities.