arrow
Return

Concrete Strength Prediction Using Machine Learning and Somersaulting Spider Optimizer

delete2026-01-01
delete1
PRE
AI
E
Eid, Marwa M. *
A
Alhussan, Amel Ali
M
Mattar, Ebrahim A.
K
Khodadadi, Nima *
E
El-Kenawy, El-Sayed M.
DOI:10.32604/cmes.2025.073555delete
deleteOriginal
deleteOriginal request for help
deleteShare
deleteSave
Abstract

Abstract

En 中文
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.
Keywords:
Concrete strength
machine learning
CatBoost
metaheuristic optimization
somersaulting spider optimizer
ensemble models

Journal

C
CMES-COMPUTER MODELING IN ENGINEERING & SCIENCES
IF:
2.5
Papers:
344
Citations:
0

Organization

A
Applied Science Private University
Scholars:
422
Papers: 356
Citations: 1.5K
D
delta higher institute for engineering & technology
Scholars:
81
Papers: 104
Citations: 0
U
University of Bahrain
Scholars:
156
Papers: 105
Citations: 1.3K
U
university of miami
Scholars:
3.4W
Papers: 2.6W
Citations: 32
D
delta university for science & technology
Scholars:
51
Papers: 41
Citations: 0
researcher View more organizations