Prediction And Analysis Of Concrete Compressive Strength By Machine Learning Methods

Remzi Gürfidan; Kemal Erten

doi:10.17798/bitlisfen.1816694

Prediction And Analysis Of Concrete Compressive Strength By Machine Learning Methods

Abstract

Concrete compressive strength (CCS) is a critical parameter directly affecting the load-bearing capacity, durability, and overall safety of engineering structures. Traditional experimental approaches for determining CCS are time-consuming and costly, making predictive models an attractive alternative. In this study, thirteen different machine learning algorithms were applied to a well-established dataset (1030 samples, 8 input parameters) to estimate concrete compressive strength. Unlike many previous studies using the Yeh dataset that primarily emphasize prediction accuracy of individual models, this work presents a systematic multi-model comparison within a unified hyperparameter optimization framework. In addition to conventional performance metrics, permutation importance and SHAP-based explainability analyses are jointly employed, and detailed error evaluations are conducted across curing age and water-to-binder ratio subgroups to enhance engineering interpretability. Among the models tested, the CatBoost algorithm demonstrated the highest predictive performance (R² = 0.9469, RMSE = 3.70), followed closely by XGBoost, Gradient Boosting, and a stacking ensemble model. The results highlight that boosting-based machine learning models not only achieve high accuracy but also provide interpretable and robust predictions when evaluated through comprehensive error and explainability analyses.

Keywords

Ethical Statement

This study does not require ethics committee permission or any special permission.

Thanks

The authors have not received any financial support for the research, authorship, or publication of this study.

References

Mehta, P. K., & Monteiro, P. J. M. Concrete Microstructure, Properties, and Materials. McGraw-Hill, 2006.
Nguyen, H., et al. “Efficient machine learning models for prediction of concrete strengths.” Construction and Building Materials, 266 (2021) 120950.
Meyer, C. “The greening of the concrete industry.” Cement and Concrete Composites, 31(8) (2009) 601–605.
Aprianti, E. “A huge number of artificial waste material can be supplementary cementitious material (SCM) for concrete production–a review part II.” Journal of Cleaner Production, 142 (2017) 4178–4194.
Feng, D.-C., et al. “Machine learning-based compressive strength prediction for concrete: An adaptive boosting approach.” Construction and Building Materials, 230 (2020) 117000.
Ziolkowski, P., & Niedostatkiewicz, M. “Machine learning techniques in concrete mix design.” Materials, 12(8) (2019) 1256.
Asteris, P. G., et al. “Soft computing techniques for the prediction of concrete compressive strength using Non-Destructive tests.” Construction and Building Materials, 303 (2021) 124450.
Binici, H., Çağatay, İ. H., & Kaplan, H. “Değişik faktörlerin beton mukevemetine etkisinin deneysel olarak incelenmesi.” Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 6(3) (2000) 203–209.

Yücel, K. T., & Erten, K. M. “Effects of Additives on Concrete-Rebar Adherence.” International Scientific and Vocational Studies Journal, 7(1) (2023) 47–54.
Sharma, A., & Ahmad, J. “Factors affecting compressive strength of geopolymer concrete-a review.” International Research Journal of Engineering and Technology, 4(4) (2017).
Wang, W., & Yue, Q. “The time variation law of concrete compressive strength: A review.” Applied Sciences, 13(8) (2023) 4947.
Naseri, H., et al. “Designing sustainable concrete mixture by developing a new machine learning technique.” Journal of Cleaner Production, 258 (2020) 120578.
Tahmouresi, B., et al. “Mechanical strength and microstructure of engineered cementitious composites: A new configuration for direct tensile strength, experimental and numerical analysis.” Construction and Building Materials, 269 (2021) 121361.
Kanamarlapudi, L., et al. “Different mineral admixtures in concrete: a review.” SN Applied Sciences, 2(4) (2020) 760.
Prokopski, G., & Langier, B. “Effect of water/cement ratio and silica fume addition on the fracture toughness and morphology of fractured surfaces of gravel concretes.” Cement and Concrete Research, 30(9) (2000) 1427–1433.
Sadegh-Zadeh, S.-A., et al. “Machine learning modelling for compressive strength prediction of superplasticizer-based concrete.” Infrastructures, 8(2) (2023) 21.
Sadrmomtazi, A., Lotfi-Omran, O., & Nikbin, I. M. “On the fracture parameters of heavy-weight magnetite concrete with different water-cement ratios through three methods.” Engineering Fracture Mechanics, 219 (2019) 106615.
Kumar, R., & Bhattacharjee, B. “Porosity, pore size distribution and in situ strength of concrete.” Cement and Concrete Research, 33(1) (2003) 155–164.
Öztaş, A., et al. “Predicting the compressive strength and slump of high strength concrete using neural network.” Construction and Building Materials, 20(9) (2006) 769–775.
Uygunoğlu, T., & Ünal, O. “Seyitömer uçucu külünün betonun basınç dayanımına etkisi üzerine bulanık mantık yaklaşımı.” Yapı Teknolojileri Elektronik Dergisi, 1(1) (2005) 13–20.
Pourbaba, M., et al. “Effect of age on the compressive strength of ultra-high-performance fiber-reinforced concrete.” Construction and Building Materials, 175 (2018) 402–410.
Chopra, P., et al. “Comparison of machine learning techniques for the prediction of compressive strength of concrete.” Advances in Civil Engineering, 2018 (2018) 5481705.
Tran, V. Q., Dang, V. Q., & Ho, L. S. “Evaluating compressive strength of concrete made with recycled concrete aggregates using machine learning approach.” Construction and Building Materials, 323 (2022) 126578.
Moein, M. M., et al. “Predictive models for concrete properties using machine learning and deep learning approaches: A review.” Journal of Building Engineering, 63 (2023) 105444.
Yeh, I.-C. “Modeling of strength of high-performance concrete using artificial neural networks.” Cement and Concrete Research, 28(12) (1998) 1797–1808.
Kadir Güçlüer, A. Ö., Göymen, S., & Günaydın, O. “A comparative investigation using machine learning methods for concrete compressive strength estimation.” Materials Today Communications, 27 (2021).
Asteris, P. G., et al. “Predicting concrete compressive strength using hybrid ensembling of surrogate machine learning models.” Cement and Concrete Research, 145 (2021) 106449.
Paudel, S., et al. “Compressive strength of concrete material using machine learning techniques.” Cleaner Engineering and Technology, 15 (2023) 100661.
Beskopylny, A. N., et al. “Prediction of the compressive strength of vibrocentrifuged concrete using machine learning methods.” Buildings, 14(2) (2024) 377.
Abrams, D. A. Design of Concrete Mixtures, Vol. 1. Structural Materials Research Laboratory, Lewis Institute, 1919.
Aïtcin, P.-C. High Performance Concrete. CRC Press, 1998.
Hoerl, A. E., & Kennard, R. W. “Ridge regression: Biased estimation for nonorthogonal problems.” Technometrics, 12(1) (1970) 55–67.
Tibshirani, R. “Regression shrinkage and selection via the lasso.” Journal of the Royal Statistical Society Series B: Statistical Methodology, 58(1) (1996) 267–288.
Zou, H., & Hastie, T. “Regularization and variable selection via the elastic net.” Journal of the Royal Statistical Society Series B: Statistical Methodology, 67(2) (2005) 301–320.
Smola, A. J., & Schölkopf, B. “A tutorial on support vector regression.” Statistics and Computing, 14(3) (2004) 199–222.
Breiman, L. “Random forests.” Machine Learning, 45(1) (2001) 5–32.
Friedman, J. H. “Greedy function approximation: a gradient boosting machine.” Annals of Statistics, (2001) 1189–1232.
Chen, T., & Guestrin, C. “XGBoost: A scalable tree boosting system.” in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016.
Ke, G., et al. “LightGBM: A highly efficient gradient boosting decision tree.” Advances in Neural Information Processing Systems, 30 (2017).
Prokhorenkova, L., et al. “CatBoost: unbiased boosting with categorical features.” Advances in Neural Information Processing Systems, 31 (2018).
Breiman, L. “Stacked regressions.” Machine Learning, 24(1) (1996) 49–64.
Wolpert, D. H. “Stacked generalization.” Neural Networks, 5(2) (1992) 241–259.
Bergstra, J., & Bengio, Y. “Random search for hyper-parameter optimization.” Journal of Machine Learning Research, 13(1) (2012) 281–305.
Kohavi, R. “A study of cross-validation and bootstrap for accuracy estimation and model selection.” in IJCAI, 1995, Montreal, Canada.
Stone, M. “Cross-validatory choice and assessment of statistical predictions.” Journal of the Royal Statistical Society: Series B (Methodological), 36(2) (1974) 111–133.
Fisher, A., Rudin, C., & Dominici, F. “All models are wrong, but many are useful: Learning a variable's importance by studying an entire class of prediction models simultaneously.” Journal of Machine Learning Research, 20(177) (2019) 1–81.
Strobl, C., et al. “Bias in random forest variable importance measures: Illustrations, sources and a solution.” BMC Bioinformatics, 8(1) (2007) 25.
ACI, Guide for modeling and calculating shrinkage and creep of concrete (ACI 209.2R-08). American Concrete Institute, Farmington Hills, MI, 2008.
Kutner, M. H. Applied Linear Statistical Models. 2005.
Bishop, C. M., & Nasrabadi, N. M. Pattern Recognition and Machine Learning, Vol. 4. Springer, 2006.
Chai, T., & Draxler, R. R. “Root mean square error (RMSE) or mean absolute error (MAE)?–Arguments against avoiding RMSE in the literature.” Geoscientific Model Development, 7(3) (2014) 1247–1250.
Willmott, C. J., & Matsuura, K. “Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance.” Climate Research, 30(1) (2005) 79–82.
De Myttenaere, A., et al. “Mean absolute percentage error for regression models.” Neurocomputing, 192 (2016) 38–48.
Pranav, S., Lahoti, M., & Gopalarathnam, M. “Concrete compressive strength prediction using boosting algorithms,” in Fiber Reinforced Polymeric Materials and Sustainable Structures, Springer, 2023, pp. 307–315.
Suresh, H., & Guttag, J. V. “A framework for understanding unintended consequences of machine learning.” arXiv preprint arXiv:1901.10002, 2019.

Details

Primary Language

English

Subjects

Construction Materials

Journal Section

Research Article

Authors

Remzi Gürfidan ^*
0000-0002-4899-2219
Türkiye

Kemal Erten
0000-0001-5181-4109
Türkiye

Publication Date

June 30, 2026

Submission Date

November 3, 2025

Acceptance Date

February 27, 2026

Published in Issue

Year 2026 Volume: 15 Number: 2

DOI

https://doi.org/10.17798/bitlisfen.1816694

IZ

https://izlik.org/JA65HP66UZ

Cite

RIS / Bibtex

APA

Gürfidan, R., & Erten, K. (2026). Prediction And Analysis Of Concrete Compressive Strength By Machine Learning Methods. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, 15(2), 669-685. https://doi.org/10.17798/bitlisfen.1816694

AMA

1.Gürfidan R, Erten K. Prediction And Analysis Of Concrete Compressive Strength By Machine Learning Methods. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi. 2026;15(2):669-685. doi:10.17798/bitlisfen.1816694

Chicago

Gürfidan, Remzi, and Kemal Erten. 2026. “Prediction And Analysis Of Concrete Compressive Strength By Machine Learning Methods”. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi 15 (2): 669-85. https://doi.org/10.17798/bitlisfen.1816694.

EndNote

Gürfidan R, Erten K (June 1, 2026) Prediction And Analysis Of Concrete Compressive Strength By Machine Learning Methods. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi 15 2 669–685.

IEEE

[1]R. Gürfidan and K. Erten, “Prediction And Analysis Of Concrete Compressive Strength By Machine Learning Methods”, Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, vol. 15, no. 2, pp. 669–685, June 2026, doi: 10.17798/bitlisfen.1816694.

ISNAD

Gürfidan, Remzi - Erten, Kemal. “Prediction And Analysis Of Concrete Compressive Strength By Machine Learning Methods”. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi 15/2 (June 1, 2026): 669-685. https://doi.org/10.17798/bitlisfen.1816694.

JAMA

1.Gürfidan R, Erten K. Prediction And Analysis Of Concrete Compressive Strength By Machine Learning Methods. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi. 2026;15:669–685.

MLA

Gürfidan, Remzi, and Kemal Erten. “Prediction And Analysis Of Concrete Compressive Strength By Machine Learning Methods”. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, vol. 15, no. 2, June 2026, pp. 669-85, doi:10.17798/bitlisfen.1816694.

Vancouver

1.Remzi Gürfidan, Kemal Erten. Prediction And Analysis Of Concrete Compressive Strength By Machine Learning Methods. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi. 2026 Jun. 1;15(2):669-85. doi:10.17798/bitlisfen.1816694