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

ABSTRACT: 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. Addition

SUBMITTER: Rathakrishnan V 

PROVIDER: S-EPMC9184605 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature