Investigation of interfacial bond strength of circular CFST columns based on ordinary and inorganic polymers through advanced machine learning methods

Concrete-filled steel tube (CFST) is a combination of materials, in which the interfacial bond-slip behavior is the prerequisite to reflect the synergistic loading of external steel tube and core concrete. The inorganic polymer concrete within the concrete-filled steel tube (IPCFST) compensates for the drying shrinkage of the concrete and enhances the adhesion at the interface between the steel tube and the concrete. However, the current code is too simple for the bond strength of CFSTs, and the existing theoretical calculation methods are not universally applicable. This paper aims to develop a practical artificial neural network tool for predicting the interfacial bond strength of ordinary CFSTs and IPCFSTs. An efficient prediction model for the bond strength of CFST interfaces was developed using a radial basis function network (RBFNN), with concrete strength, steel tube size, bond length and other factors as the main parameters. Bond performance test data was collected for a sample database of 322 circular CFST columns, including 56 ordinary CFSTs and 263 IPCFSTs. The results show that the Artificial Neural Network (ANN) algorithm can effectively address the bond strength problem of CFST structures and improve the accuracy and density of prediction in the comparative analyses with the measured values and the results of the semi-theoretical formulations. Based on the prediction results of ANN model, a sensitivity analysis of the parameters using Garson's algorithm shows that the diameter-to-thickness ratio and concrete strength significantly affect bond strength for both ordinary CFSTs and IPCFSTs. This study provides a more reliable prediction method and technical support for the engineering design of CFST structures.