Machine learning-based prediction of CFST columns using gradient tree boosting algorithm

Abstract Among recent artificial intelligence techniques, machine learning (ML) has gained significant attention during the past decade as an emerging topic in civil and structural engineering. This paper presents an efficient and powerful machine learning-based framework for strength predicting of concrete filled steel tubular (CFST) columns under concentric loading. The proposed framework was based on the gradient tree boosting (GTB) algorithm which is one of the most powerful ML techniques for developing predictive models. A comprehensive database of over 1,000 tests on circular CFST columns was also collected from the open literature to serve as training and testing purposes of the developed framework. The efficiency of the proposed framework was demonstrated by comparing its performance with that obtained from other ML methods such as random forest (RF), support vector machines (SVM), decision tree (DT) and deep learning (DL). The accuracy of the developed predictive model was also verified with the current design equations from modern codes of practice as well as existing ML-based predictive models.