Using machine learning algorithms to estimate the compressive property of high strength fiber reinforced concrete

Dai, Li and Wu, Xu and Zhou, Meirong and Ahmad, Waqas and Ali, Mujahid and Sabri, Mohanad Muayad Sabri and Salmi, Abdelatif and Ewais, Dina Yehia Zakaria (2022) Using machine learning algorithms to estimate the compressive property of high strength fiber reinforced concrete. Materials, 15 (13). ISSN 1996-1944, DOI

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The low tensile strain capacity and brittle nature of high-strength concrete (HSC) can be improved by incorporating steel fibers into it. Steel fibers' addition in HSC results in bridging behavior which improves its post-cracking behavior, provides cracks arresting and stresses transfer in concrete. Using machine learning (ML) techniques, concrete properties prediction is an effective solution to conserve construction time and cost. Therefore, sophisticated ML approaches are applied in this study to predict the compressive strength of steel fiber reinforced HSC (SFRHSC). To fulfil this purpose, a standalone ML model called Multiple-Layer Perceptron Neural Network (MLPNN) and ensembled ML algorithms named Bagging and Adaptive Boosting (AdaBoost) were employed in this study. The considered parameters were cement content, fly ash content, slag content, silica fume content, nano-silica content, limestone powder content, sand content, coarse aggregate content, maximum aggregate size, water content, super-plasticizer content, steel fiber content, steel fiber diameter, steel fiber length, and curing time. The application of statistical checks, i.e., root mean square error (RMSE), determination coefficient (R-2), and mean absolute error (MAE), was also performed for the assessment of algorithms' performance. The study demonstrated the suitability of the Bagging technique in the prediction of SFRHSC compressive strength. Compared to other models, the Bagging approach was more accurate as it produced higher, i.e., 0.94, R-2, and lower error values. It was revealed from the SHAP analysis that curing time and super-plasticizer content have the most significant influence on the compressive strength of SFRHSC. The outcomes of this study will be beneficial for researchers in civil engineering for the timely and effective evaluation of SFRHSC compressive strength.

Item Type: Article
Funders: Ministry of Science and Higher Education of the Russian Federation as part of the World-class Research Center program: Advanced Digital Technologies (Grant No: 075-15-2022-311)
Uncontrolled Keywords: Steel fiber; Concrete; High strength concrete; Compressive strength; Building material
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Divisions: Faculty of Engineering > Department of Civil Engineering
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 19 Oct 2023 09:18
Last Modified: 19 Oct 2023 09:18

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