Publication: Boosting-based ensemble machine learning models for predicting unconfined compressive strength of geopolymer stabilized clayey soil
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Date
2024
Authors
Abdullah G.M.S.
Ahmad M.
Babur M.
Badshah M.U.
Al-Mansob R.A.
Gamil Y.
Fawad M.
Journal Title
Journal ISSN
Volume Title
Publisher
Nature Research
Abstract
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).
Description
Keywords
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