Concrete Strength Prediction Using Machine Learning and Somersaulting Spider Optimizer

Marwa M. Eid^1,2,*, Amel Ali Alhussan³, Ebrahim A. Mattar⁴, Nima Khodadadi^5,*, El-Sayed M. El-Kenawy^6,7
1 Faculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura, 11152, Egypt
2 Jadara Research Center, Jadara University, Irbid, 21110, Jordan
3 Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia
4 College of Engineering, University of Bahrain, Sakhir, P.O. Box 32038, Kingdom of Bahrain
5 Department of Civil and Architectural Engineering, University of Miami, Coral Gables, FL 33146, USA
6 Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura, 35111, Egypt
7 Applied Science Research Center, Applied Science Private University, Amman, 11931, Jordan
* Corresponding Author: Marwa M. Eid. Email: email ; Nima Khodadadi. Email: email
(This article belongs to the Special Issue: Swarm and Metaheuristic Optimization for Applied Engineering Application)

Computer Modeling in Engineering & Sciences https://doi.org/10.32604/cmes.2025.073555

Received 20 September 2025; Accepted 29 December 2025; Published online 09 January 2026

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Abstract

Accurate prediction of concrete compressive strength is fundamental for optimizing mix designs, improving material utilization, and ensuring structural safety in modern construction. Traditional empirical methods often fail to capture the non-linear relationships among concrete constituents, especially with the growing use of supplementary cementitious materials and recycled aggregates. This study presents an integrated machine learning framework for concrete strength prediction, combining advanced regression models—namely CatBoost—with metaheuristic optimization algorithms, with a particular focus on the Somersaulting Spider Optimizer (SSO). A comprehensive dataset encompassing diverse mix proportions and material types was used to evaluate baseline machine learning models, including CatBoost, XGBoost, ExtraTrees, and RandomForest. Among these, CatBoost demonstrated superior accuracy across multiple performance metrics. To further enhance predictive capability, several bio-inspired optimizers were employed for hyperparameter tuning. The SSO-CatBoost hybrid achieved the lowest mean squared error and highest correlation coefficients, outperforming other metaheuristic approaches such as Genetic Algorithm, Particle Swarm Optimization, and Grey Wolf Optimizer. Statistical significance was established through Analysis of Variance and Wilcoxon signed-rank testing, confirming the robustness of the optimized models. The proposed methodology not only delivers improved predictive performance but also offers a transparent framework for mix design optimization, supporting data-driven decision making in sustainable and resilient infrastructure development.