Predicting Concrete Strength Using Regression-Based Machine Learning Techniques

International Journal of Civil Engineering

© 2025 by SSRG - IJCE Journal

Volume 12 Issue 10

Year of Publication : 2025

Authors : S.Selvi, S.E. Murthy, Balaji Govindan, D. Leela Rani, Mutyala Suresh

10.14445/23488352/IJCE-V12I10P111

How to Cite?

S.Selvi, S.E. Murthy, Balaji Govindan, D. Leela Rani, Mutyala Suresh, "Predicting Concrete Strength Using Regression-Based Machine Learning Techniques," SSRG International Journal of Civil Engineering, vol. 12,  no. 10, pp. 139-146, 2025. Crossref, https://doi.org/10.14445/23488352/IJCE-V12I10P111

Abstract:

Accurate prediction of concrete Compressive Strength (CS) is crucial for optimizing mix design and ensuring structural reliability. The prediction of concrete Strength remains challenging owing to the complex relationship between the components of the concrete mixture. Although several traditional methods are available, they are mostly experiment-based and are expensive and often inaccurate. This research employs Machine Learning (ML) approaches to estimate the Compressive Strength of concrete (CS) and to analyse how the input parameters influence the output response. Five tree-based regression algorithms, such as Random Forest and other boosting variants, were assessed to determine their predictive capability. The dataset contains 1030 samples with features such as water, fly ash, cement, age, coarse and fine aggregates, superplasticizer, and slag. Serve as inputs for developing the ML models. The model's accuracy is evaluated by metrics such as R², RMSE, and MAE. Based on the evaluation metrics, CatBoost is the best-performing model for predicting concrete compression strength. SHAP analysis further revealed that the age of the concrete and the amount of cement used are the most influential factors.

Keywords:

Artificial Intelligence, Catboost, Compressive Strength, Machine Learning, Shap.

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