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

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 concr...

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Autores principales:	Nadia Moneem Al-Abdaly, Salwa R. Al-Taai, Hamza Imran, Majed Ibrahim
Formato:	article
Lenguaje:	EN RU UK
Publicado:	PC Technology Center 2021
Materias:	machine learning random forest fiber-reinforced concrete compressive strength Technology (General) T1-995 Industry HD2321-4730.9
Acceso en línea:	https://doaj.org/article/c86932b3e07e4101964d6561d32e895a
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spelling	oai:doaj.org-article:c86932b3e07e4101964d6561d32e895a2021-11-04T14:08:24ZDevelopment of prediction model of steel fiber-reinforced concrete compressive strength using random forest algorithm combined with hyperparameter tuning and k-fold cross-validation1729-37741729-406110.15587/1729-4061.2021.242986https://doaj.org/article/c86932b3e07e4101964d6561d32e895a2021-10-01T00:00:00Zhttp://journals.uran.ua/eejet/article/view/242986https://doaj.org/toc/1729-3774https://doaj.org/toc/1729-4061Because 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 SFRCNadia Moneem Al-AbdalySalwa R. Al-TaaiHamza ImranMajed IbrahimPC Technology Centerarticlemachine learningrandom forestfiber-reinforced concretecompressive strengthTechnology (General)T1-995IndustryHD2321-4730.9ENRUUKEastern-European Journal of Enterprise Technologies, Vol 5, Iss 7 (113), Pp 59-65 (2021)
institution	DOAJ
collection	DOAJ
language	EN RU UK
topic	machine learning random forest fiber-reinforced concrete compressive strength Technology (General) T1-995 Industry HD2321-4730.9
spellingShingle	machine learning random forest fiber-reinforced concrete compressive strength Technology (General) T1-995 Industry HD2321-4730.9 Nadia Moneem Al-Abdaly Salwa R. Al-Taai Hamza Imran Majed Ibrahim Development of prediction model of steel fiber-reinforced concrete compressive strength using random forest algorithm combined with hyperparameter tuning and k-fold cross-validation
description	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
format	article
author	Nadia Moneem Al-Abdaly Salwa R. Al-Taai Hamza Imran Majed Ibrahim
author_facet	Nadia Moneem Al-Abdaly Salwa R. Al-Taai Hamza Imran Majed Ibrahim
author_sort	Nadia Moneem Al-Abdaly
title	Development of prediction model of steel fiber-reinforced concrete compressive strength using random forest algorithm combined with hyperparameter tuning and k-fold cross-validation
title_short	Development of prediction model of steel fiber-reinforced concrete compressive strength using random forest algorithm combined with hyperparameter tuning and k-fold cross-validation
title_full	Development of prediction model of steel fiber-reinforced concrete compressive strength using random forest algorithm combined with hyperparameter tuning and k-fold cross-validation
title_fullStr	Development of prediction model of steel fiber-reinforced concrete compressive strength using random forest algorithm combined with hyperparameter tuning and k-fold cross-validation
title_full_unstemmed	Development of prediction model of steel fiber-reinforced concrete compressive strength using random forest algorithm combined with hyperparameter tuning and k-fold cross-validation
title_sort	development of prediction model of steel fiber-reinforced concrete compressive strength using random forest algorithm combined with hyperparameter tuning and k-fold cross-validation
publisher	PC Technology Center
publishDate	2021
url	https://doaj.org/article/c86932b3e07e4101964d6561d32e895a
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Development of prediction model of steel fiber-reinforced concrete compressive strength using random forest algorithm combined with hyperparameter tuning and k-fold cross-validation

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