Prediction and reliability analysis of shear strength of RC deep beams.

ABSTRACT: This study explores machine learning (ML) capabilities for predicting the shear strength of reinforced concrete deep beams (RCDBs). For this purpose, eight typical machine-learning models, i.e., symbolic regression (SR), XGBoost (XGB), CatBoost (CATB), random forest (RF), LightGBM, support vector regression (SVR), artificial neural networks (ANN), and Gaussian process regression (GPR) models, are selected and compared based on a database of 840 samples with 14 input features. The hyperparameter tuning of the introduced ML models is performed using the Bayesian optimization (BO) technique. The comparison results show that the CatBoost model is the most reliable and accurate ML model (R² = 0.997 and 0.947 in the training and testing sets, respectively). In addition, simple and pra

SUBMITTER: Megahed K 

PROVIDER: S-EPMC11199521 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature