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

Nadia Moneem Al-Abdaly,Majed Ibrahim,Salwa R Al-Taai,Hamza Imran

doi:10.15587/1729-4061.2021.242986

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

Highlights

This study aims to predict the compressive strength of steel fiber-reinforced concrete (SFRC) concrete using a random forest algorithm
Our best Random forest (RF) is used to predict the compressive strength of SFRC for the testing dataset and the results are compared with the linear regression model for the same observations using performance metrics
The RF best model achieved lower root mean square error (RMSE) and higher R2 and mean absolute error (MAE) for the training dataset (RMSE=5.21, R2=0.8, and MAE=3.59) compared with the traditional linear regression model (R2=0.6, RMSE=7.7517, and MAE=5.6966). This indicates that RF outperforms the linear regression in the process of prediction of compressive strength of fiber-reinforced concrete

Summary

Introduction

The final product of common stresses such as impact, loading, and fatigue is cracking, which leads eventually to the failure of concrete. For this reason, the brittleness of concrete has been a challenge in civil engineering since the beginning. The brittleness of concrete has been a challenge in civil engineering since the beginning This difficulty has been solved thanks to advancements in concrete material technology, such as the integration of scattered fibers. Fiber-reinforced concrete (FRC) is a word that has been questioned [1,2,3]. The bridging influence of disconnected fibers in fiber-reinforced concrete (FRC) can boost the mechanical properties of standard concrete.

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