Development of prediction model of steel fiber-reinforced concrete compressive strength using random forest algorithm combined with hyperparameter tuning and k-fold cross-validation

Authors

DOI:

https://doi.org/10.15587/1729-4061.2021.242986

Keywords:

machine learning, random forest, fiber-reinforced concrete, compressive strength

Abstract

Because of the incorporation of discontinuous fibers, steel fiber-reinforced concrete (SFRC) outperforms regular concrete. However, due to its complexity and limited available data, the development of SFRC strength prediction techniques is still in its infancy when compared to that of standard concrete. In this paper, the compressive strength of steel fiber-reinforced concrete was predicted from different variables using the Random forest model. Case studies of 133 samples were used for this aim. To design and validate the models, we generated training and testing datasets. The proposed models were developed using ten important material parameters for steel fiber-reinforced concrete characterization. To minimize training and testing split bias, the approach used in this study was validated using the 10-fold Cross-Validation procedure. To determine the optimal hyperparameters for the Random Forest algorithm, the Grid Search Cross-Validation approach was utilized. The root mean square error (RMSE), coefficient of determination (R2), and mean absolute error (MAE) between measured and estimated values were used to validate and compare the models. The prediction performance with RMSE=5.66, R2=0.88 and MAE=3.80 for the Random forest model. Compared with the traditional linear regression model, the outcomes showed that the Random forest model is able to produce enhanced predictive results of the compressive strength of steel fiber-reinforced concrete. The findings show that hyperparameter tuning with grid search and cross-validation is an efficient way to find the optimal parameters for the RF method. Also, RF produces good results and gives an alternate way for anticipating the compressive strength of SFRC

Author Biographies

Nadia Moneem Al-Abdaly, Al-Furat Al-Awsat Technical University

Doctor of Engineering, Building Material, Assistant Professor

Department of Civil

Najaf Technical Institute

Salwa R. Al-Taai, Mustansiriyah University

Master of Civil Engineering

Department of Civil Engineering

College of Engineering

Hamza Imran, Al-karkh University of Science

Master of Civil Engineering, Assistant Lecturer

Department of Construction and Project

Majed Ibrahim, Institute of Earth and Environmental Sciences

PhD, Associate Professor

Department of Geographic Information Systems and Remote Sensing

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Published

2021-10-29

How to Cite

Al-Abdaly, N. M., Al-Taai, S. R., Imran, H., & Ibrahim, M. (2021). Development of prediction model of steel fiber-reinforced concrete compressive strength using random forest algorithm combined with hyperparameter tuning and k-fold cross-validation. Eastern-European Journal of Enterprise Technologies, 5(7 (113), 59–65. https://doi.org/10.15587/1729-4061.2021.242986

Issue

Vol. 5 No. 7 (113) (2021): Applied mechanics

Section

Applied mechanics

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Copyright (c) 2021 Nadia Moneem Al-Abdaly, Salwa R. Al-Taai, Hamza Imran, Majed Ibrahim

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