Machine-learning-based predictive models for concrete-filled double skin tubular columns

• Published in: Engineering structures Vol. 304; p. 117593
• Main Authors: Zarringol, Mohammadreza, Patel, Vipulkumar Ishvarbhai, Liang, Qing Quan, Hassanein, M.F., Ahmed, Mizan
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.04.2024
• Subjects: ANN; Concrete-filled double skin tubular (CFDST); Machine learning; SHAP; XGBoost; ANN; SHAP; XGBoost; Concrete-filled double skin tubular (CFDST); Machine learning
• ISSN: 0141-0296; 1873-7323
• DOI: 10.1016/j.engstruct.2024.117593

This paper aims to develop a unique artificial neural network (ANN)-based equation as well as MATLAB- and Python-based graphical user interfaces (GUIs) using the most comprehensive and up-to-date database for predicting the behaviour of axially loaded concrete-filled double skin tubular (CFDST) short and slender columns with normal- and high-strength materials. Two machine learning (ML) methods, which are ANN and extreme gradient boosting (XGBoost), are trained and tested using 1721 sets of data, with 129 of them collected from experimental studies and 1592 generated by finite element (FE) simulations. The accuracy of the developed ML models is assessed through comparing their predictions with the experimental and FE results. To demonstrate the effect of each parameter on the predicted results, the SHapley Additive exPlanations (SHAP) method is used. The developed ML models are also used to conduct parametric studies to examine the effect of geometric and material parameters on the predicted results. The accuracy of the ML models and the proposed ANN-based equation in predicting the ultimate axial capacity of CFDST columns is compared with that of six design methods including two design code provisions and four design equations proposed by researchers. A numerical example is presented to illustrate the design procedure of the CFDST column using the proposed ANN-based equation. The results indicate that the ANN model performs better on unseen data than the XGBoost model with lower root mean square error for the test set. The results also show that the ML models and the proposed ANN-based equation are superior to the other design models in prediction accuracy. •1721 data samples of CFDST columns are used to train and test the ML models.•Optimized ANN and XGBoost models are used to predict the behaviour of CFDST columns.•ANN-based equation, MATLAB- and Python-based GUIs are developed.•Parametric studies are conducted using the trained ML models.•The superiority of the proposed design models over six design equations is shown.