An efficient procedure for prediction of the load-displacement curve of CFDST columns

This paper proposes a novel procedure for prediction of both load-displacement curve and load-carrying capacity of concrete-filled double-skin steel tube (CFDST) columns under uniaxial compression by using convolutional neural network (CNN)-based regression and Nelder-Mead methods. Firstly, hybrid databases collected from experiments in literature and generated from finite element analyses are employed to build the proposed CNN-based model. The accuracy of the proposed model is described through a comparison between predictive results of the proposed model and unseen data. Two machine learning models, including eXtreme Gradient Boosting and Multilayer Perceptron , are adopted for comparison. It can be observed that the CNN-based model provides the most accurate predictions for both the load-displacement curve and axial compression capacity of CFDST columns in both experimental and numerical databases. An efficient procedure is developed to calibrate the preliminary load-displacement curve estimated by the CNN-based model, and to notably enhance its smoothness and performance. Adjusted formulae (based on well-known equations) are obtained for predicting the load-displacement curve of CFDST columns. The hyperparameters of these formulae are optimized using the Nelder-Mead method. It is indicated that the adjusted load-displacement curves obtained from the proposed procedure outperform the preliminary curves estimated by the CNN-based model. A sensitivity analysis was conducted to investigate the model's performance in predicting the load-displacement curves of CFDST columns with variations of input variables within stochastic environments. Finally, a cloud-based graphical user interface is developed to provide a convenient tool for users to predict axial load-displacement responses of CFDST columns without prior programming knowledge. • A novel procedure for predicting load-displacement curves of CFDST columns is proposed for the first time. • CNN-based model presents superior strength prediction performance compared to XGB and MLP models. • CNN-based model is applied to accurately predict load-displacement curves of CFDST columns. • Adjusted formulae are suggested for predicting load-displacement curves of CFDST columns. • A cloud-based GUI program is made online accessible for practical design purposes.