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Machine Learning-Based Estimation of the Compressive Strength of Self-Compacting Concrete: A Multi-Dataset Study

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  • Nhat-Duc Hoang

    (Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
    Faculty of Civil Engineering, Duy Tan University, Da Nang 550000, Vietnam)

Abstract

This paper aims at performing a comparative study to investigate the predictive capability of machine learning (ML) models used for estimating the compressive strength of self-compacting concrete (SCC). Seven prominent ML models, including deep neural network regression (DNNR), extreme gradient boosting machine (XGBoost), gradient boosting machine (GBM), adaptive boosting machine (AdaBoost), support vector regression (SVR), Levenberg–Marquardt artificial neural network (LM-ANN), and genetic programming (GP), are employed. Four experimental datasets, compiled in previous studies, are used to construct the ML-based methods. The models’ generalization capabilities are reliably evaluated by 20 independent runs. Experimental results point out the superiority of the DNNR, which has excelled other models in three out of four datasets. The XGBoost is the second-best model, which has gained the first rank in one dataset. The outcomes point out the great potential of the utilized ML approaches in modeling the compressive strength of SCC. In more details, the coefficient of determination ( R 2 ) surpasses 0.8 and the mean absolute percentage error (MAPE) is always below 15% for all datasets. The best results of R 2 and MAPE are 0.93 and 7.2%, respectively.

Suggested Citation

  • Nhat-Duc Hoang, 2022. "Machine Learning-Based Estimation of the Compressive Strength of Self-Compacting Concrete: A Multi-Dataset Study," Mathematics, MDPI, vol. 10(20), pages 1-20, October.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:20:p:3771-:d:940935
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    References listed on IDEAS

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    1. Tuan Anh Pham & Van Quan Tran & Huong-Lan Thi Vu & Hai-Bang Ly, 2020. "Design deep neural network architecture using a genetic algorithm for estimation of pile bearing capacity," PLOS ONE, Public Library of Science, vol. 15(12), pages 1-25, December.
    2. Friedman, Jerome H., 2002. "Stochastic gradient boosting," Computational Statistics & Data Analysis, Elsevier, vol. 38(4), pages 367-378, February.
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    1. Nahushananda Chakravarthy H G & Karthik M Seenappa & Sujay Raghavendra Naganna & Dayananda Pruthviraja, 2023. "Machine Learning Models for the Prediction of the Compressive Strength of Self-Compacting Concrete Incorporating Incinerated Bio-Medical Waste Ash," Sustainability, MDPI, vol. 15(18), pages 1-22, September.

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