Predicting compressive strength of high-performance concrete with high volume ground granulated blast-furnace slag replacement using boosting machine learning algorithms

algorithm; article; compressive strength; furnace; machine learning; prediction error; slag

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Main Authors:	Rathakrishnan V., Bt. Beddu S., Ahmed A.N.
Other Authors:	57735393300
Format:	Article
Published:	Nature Research 2023
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Institution:	Universiti Tenaga Nasional

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spelling	my.uniten.dspace-266582023-05-29T17:36:05Z Predicting compressive strength of high-performance concrete with high volume ground granulated blast-furnace slag replacement using boosting machine learning algorithms Rathakrishnan V. Bt. Beddu S. Ahmed A.N. 57735393300 57735276200 57214837520 algorithm; article; compressive strength; furnace; machine learning; prediction error; slag Predicting the compressive strength of concrete is a complicated process due to the heterogeneous mixture of concrete and high variable materials. Researchers have predicted the compressive strength of concrete for various mixes using machine learning and deep learning models. In this research, compressive strength of high-performance concrete with high volume ground granulated blast-furnace slag replacement is predicted using boosting machine learning (BML) algorithms, namely, Light Gradient Boosting Machine, CatBoost Regressor, Gradient Boosting Regressor (GBR), Adaboost Regressor, and Extreme Gradient Boosting. In these studies, the BML model�s performance is evaluated based on prediction accuracy and prediction error rates, i.e., R2, MSE, RMSE, MAE, RMSLE, and MAPE. Additionally, the BML models were further optimised with Random Search algorithms and compared to BML models with default hyperparameters. Comparing all 5 BML models, the GBR model shows the highest prediction accuracy with R2 of 0.96 and lowest model error with MAE and RMSE of 2.73 and 3.40, respectively for test dataset. In conclusion, the GBR model are the best performing BML for predicting the compressive strength of concrete with the highest prediction accuracy, and lowest modelling error. � 2022, The Author(s). Final 2023-05-29T09:36:05Z 2023-05-29T09:36:05Z 2022 Article 10.1038/s41598-022-12890-2 2-s2.0-85131711324 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85131711324&doi=10.1038%2fs41598-022-12890-2&partnerID=40&md5=a9f8df3f2a58922fac0983f2d462992e https://irepository.uniten.edu.my/handle/123456789/26658 12 1 9539 All Open Access, Gold Nature Research Scopus
institution	Universiti Tenaga Nasional
building	UNITEN Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Tenaga Nasional
content_source	UNITEN Institutional Repository
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description	algorithm; article; compressive strength; furnace; machine learning; prediction error; slag
author2	57735393300
author_facet	57735393300 Rathakrishnan V. Bt. Beddu S. Ahmed A.N.
format	Article
author	Rathakrishnan V. Bt. Beddu S. Ahmed A.N.
spellingShingle	Rathakrishnan V. Bt. Beddu S. Ahmed A.N. Predicting compressive strength of high-performance concrete with high volume ground granulated blast-furnace slag replacement using boosting machine learning algorithms
author_sort	Rathakrishnan V.
title	Predicting compressive strength of high-performance concrete with high volume ground granulated blast-furnace slag replacement using boosting machine learning algorithms
title_short	Predicting compressive strength of high-performance concrete with high volume ground granulated blast-furnace slag replacement using boosting machine learning algorithms
title_full	Predicting compressive strength of high-performance concrete with high volume ground granulated blast-furnace slag replacement using boosting machine learning algorithms
title_fullStr	Predicting compressive strength of high-performance concrete with high volume ground granulated blast-furnace slag replacement using boosting machine learning algorithms
title_full_unstemmed	Predicting compressive strength of high-performance concrete with high volume ground granulated blast-furnace slag replacement using boosting machine learning algorithms
title_sort	predicting compressive strength of high-performance concrete with high volume ground granulated blast-furnace slag replacement using boosting machine learning algorithms
publisher	Nature Research
publishDate	2023
_version_	1806428174751367168

Predicting compressive strength of high-performance concrete with high volume ground granulated blast-furnace slag replacement using boosting machine learning algorithms

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