Muhammad Salman Khan ¹ Tianbo Peng ^1,2 Muhammad Adeel Khan ^1* Asad Khan ^1* Mahmood Ahmad ^3,4* Kamran Aziz ⁵ Mohanad Muayad Sabri Sabri ⁶ N.S Abd El-Gawaad ⁷

• ¹ College of Civil Engineering, Department of Bridge Engineering, Tongji University, Shanghai 200092, China
• ² State Key Laboratory of Disaster Reduction in Civil Engineering, Tongji University, Shanghai 200092, China
• ³ Department of Civil Engineering, University of Engineering and Technology Peshawar (Bannu Campus), Bannu 28100, Pakistan
• ⁴ Institute of Energy Infrastructure, Universiti Tenaga Nasional, Kajang 43000, Malaysia
• ⁵ The Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, 4800 Cao’an Road, Jiading, Shanghai 201804, China
• ⁶ Peter the Great St. Petersburg Polytechnic University, 195251 St. Petersburg, Russia
• ⁷ Muhayil Asir, Applied College , King Khalid University, Abha 62529, Saudi Arabia

Accurately predicting key engineering properties, such as compressive and tensile strength, remains a significant challenge in high-performance concrete (HPC) due to its complex and heterogeneous composition. Early selection of optimal components and the development of reliable machine learning (ML) models can significantly reduce the time and cost associated with extensive experimentation. This study introduces four explainable Automated Machine Learning (AutoML) models that integrate Optuna for hyperparameter optimization, SHapley Additive exPlanations (SHAP) for interpretability, and ensemble learning algorithms such as Random Forest (RF), Extreme Gradient Boosting (XGB), Light Gradient Boosting Machine (LGB), and Categorical Gradient Boosting (CB).The resulting interpretable AutoML models O-RF, O-XGB, O-LGB, and O-CB are applied to predict the compressive and tensile strengths of HPC. Compared to a baseline model from the literature, O-LGB achieved significant improvements in predictive performance. For compressive strength, it reduced the Mean Absolute Error (MAE) by 87.69% and the Root Mean Squared Error (RMSE) by 71.93%. For tensile strength, it achieved a 99.41% improvement in MAE and a 96.67% reduction in RMSE, along with increases in R². Furthermore, SHAP analysis identified critical factors influencing strength, such as cement content, water, and age for compressive strength, and curing age, water-binder ratio, and water-cement ratio for tensile strength. This approach provides civil engineers with a robust and interpretable tool for optimizing HPC properties, reducing experimentation costs, and supporting enhanced decision-making in structural design, risk assessment, and other applications.