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Boosting-based ensemble machine learning models for predicting unconfined compressive strength of geopolymer stabilized clayey soil

The present research employs new boosting-based ensemble machine learning models i.e., gradient boosting (GB) and adaptive boosting (AdaBoost) to predict the unconfined compressive strength (UCS) of geopolymer stabilized clayey soil. The GB and AdaBoost models were developed and validated using 270...

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Main Authors:	Abdullah G.M.S., Ahmad M., Babur M., Badshah M.U., Al-Mansob R.A., Gamil Y., Fawad M.
Other Authors:	56606096100
Format:	Article
Published:	Nature Research 2025
Subjects:	alkali sodium hydroxide article artificial neural network compressive strength data base fly ash furnace human machine learning mean absolute error mean squared error random forest reliability root mean squared error sensitivity analysis slag soil
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spelling	my.uniten.dspace-361902025-03-03T15:41:32Z Boosting-based ensemble machine learning models for predicting unconfined compressive strength of geopolymer stabilized clayey soil Abdullah G.M.S. Ahmad M. Babur M. Badshah M.U. Al-Mansob R.A. Gamil Y. Fawad M. 56606096100 58731610900 57191503633 58849655200 55566434500 57191379149 57949408000 alkali sodium hydroxide article artificial neural network compressive strength data base fly ash furnace human machine learning mean absolute error mean squared error random forest reliability root mean squared error sensitivity analysis slag soil The present research employs new boosting-based ensemble machine learning models i.e., gradient boosting (GB) and adaptive boosting (AdaBoost) to predict the unconfined compressive strength (UCS) of geopolymer stabilized clayey soil. The GB and AdaBoost models were developed and validated using 270 clayey soil samples stabilized with geopolymer, with ground-granulated blast-furnace slag and fly ash as source materials and sodium hydroxide solution as alkali activator. The database was randomly divided into training (80%) and testing (20%) sets for model development and validation. Several performance metrics, including coefficient of determination (R2), mean absolute error (MAE), root mean square error (RMSE), and mean squared error (MSE), were utilized to assess the accuracy and reliability of the developed models. The statistical results of this research showed that the GB and AdaBoost are reliable models based on the obtained values of R2 (= 0.980, 0.975), MAE (= 0.585, 0.655), RMSE (= 0.969, 1.088), and MSE (= 0.940, 1.185) for the testing dataset, respectively compared to the widely used artificial neural network, random forest, extreme gradient boosting, multivariable regression, and multi-gen genetic programming based models. Furthermore, the sensitivity analysis result shows that ground-granulated blast-furnace slag content was the key parameter affecting the UCS. ? 2024, The Author(s). Final 2025-03-03T07:41:32Z 2025-03-03T07:41:32Z 2024 Article 10.1038/s41598-024-52825-7 2-s2.0-85183357076 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85183357076&doi=10.1038%2fs41598-024-52825-7&partnerID=40&md5=277b14fec6355723871a1a57fea43eed https://irepository.uniten.edu.my/handle/123456789/36190 14 1 2323 All Open Access; Gold Open Access 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
url_provider	http://dspace.uniten.edu.my/
topic	alkali sodium hydroxide article artificial neural network compressive strength data base fly ash furnace human machine learning mean absolute error mean squared error random forest reliability root mean squared error sensitivity analysis slag soil
spellingShingle	alkali sodium hydroxide article artificial neural network compressive strength data base fly ash furnace human machine learning mean absolute error mean squared error random forest reliability root mean squared error sensitivity analysis slag soil Abdullah G.M.S. Ahmad M. Babur M. Badshah M.U. Al-Mansob R.A. Gamil Y. Fawad M. Boosting-based ensemble machine learning models for predicting unconfined compressive strength of geopolymer stabilized clayey soil
description	The present research employs new boosting-based ensemble machine learning models i.e., gradient boosting (GB) and adaptive boosting (AdaBoost) to predict the unconfined compressive strength (UCS) of geopolymer stabilized clayey soil. The GB and AdaBoost models were developed and validated using 270 clayey soil samples stabilized with geopolymer, with ground-granulated blast-furnace slag and fly ash as source materials and sodium hydroxide solution as alkali activator. The database was randomly divided into training (80%) and testing (20%) sets for model development and validation. Several performance metrics, including coefficient of determination (R2), mean absolute error (MAE), root mean square error (RMSE), and mean squared error (MSE), were utilized to assess the accuracy and reliability of the developed models. The statistical results of this research showed that the GB and AdaBoost are reliable models based on the obtained values of R2 (= 0.980, 0.975), MAE (= 0.585, 0.655), RMSE (= 0.969, 1.088), and MSE (= 0.940, 1.185) for the testing dataset, respectively compared to the widely used artificial neural network, random forest, extreme gradient boosting, multivariable regression, and multi-gen genetic programming based models. Furthermore, the sensitivity analysis result shows that ground-granulated blast-furnace slag content was the key parameter affecting the UCS. ? 2024, The Author(s).
author2	56606096100
author_facet	56606096100 Abdullah G.M.S. Ahmad M. Babur M. Badshah M.U. Al-Mansob R.A. Gamil Y. Fawad M.
format	Article
author	Abdullah G.M.S. Ahmad M. Babur M. Badshah M.U. Al-Mansob R.A. Gamil Y. Fawad M.
author_sort	Abdullah G.M.S.
title	Boosting-based ensemble machine learning models for predicting unconfined compressive strength of geopolymer stabilized clayey soil
title_short	Boosting-based ensemble machine learning models for predicting unconfined compressive strength of geopolymer stabilized clayey soil
title_full	Boosting-based ensemble machine learning models for predicting unconfined compressive strength of geopolymer stabilized clayey soil
title_fullStr	Boosting-based ensemble machine learning models for predicting unconfined compressive strength of geopolymer stabilized clayey soil
title_full_unstemmed	Boosting-based ensemble machine learning models for predicting unconfined compressive strength of geopolymer stabilized clayey soil
title_sort	boosting-based ensemble machine learning models for predicting unconfined compressive strength of geopolymer stabilized clayey soil
publisher	Nature Research
publishDate	2025
_version_	1825816098907357184
score	13.244413

Boosting-based ensemble machine learning models for predicting unconfined compressive strength of geopolymer stabilized clayey soil

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