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

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 an

SUBMITTER: Abdullah GMS 

PROVIDER: S-EPMC10822860 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature