Investigation of Ensemble Machine Learning Models for Estimating the Ultimate Strain of FRP-Confined Concrete Columns.

ABSTRACT: Accurately predicting the ultimate strain of fiber-reinforced polymer (FRP)-confined concrete columns is essential for the widespread application of FRP in strengthening reinforced concrete (RC) columns. This study comprehensively investigates the performance of ensemble machine learning (ML) models in estimating the ultimate strain of FRP-confined concrete (FRP-CC) columns. A dataset of 547 test results of the ultimate strain of FRP-CC columns was collected from the literature for training and testing ML models. The four best single ML models were used to develop ensemble models employing voting, stacking and bagging techniques. The performance of the ensemble models was compared with 10 single ML and 11 empirical strain models. The study results revealed that the single ML models yielded

SUBMITTER: Nguyen QT 

PROVIDER: S-EPMC12787090 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature