Ensemble Tree Machine Learning Models for Improvement of Eurocode 2 Creep Model Prediction

Abstract

Abstract Ensemble tree machine learning models have proved useful for solving poorly understood and complex problems. This paper aims to calibrate the Eurocode 2 creep model by inserting a correction coefficient to the model. The correction coefficient is calculated using ensemble tree (bagging and boosting) models. The results showed that the insertion of the correction coefficient obtained by both the bagging and boosting models into the Eurocode 2 model led to significantly higher prediction accuracy. These approaches may lead to a better performance prediction, thereby reducing the effect of the time-dependent deformation on concrete structures.