Interfacial bond capacity prediction of concrete-filled steel tubes utilizing artificial neural network

Concrete-filled steel tubes (CFSTs) have demonstrated superior performance compared to other types of composite columns. The evaluation of interactions between concrete and steel composites is crucial due to their significant impact on the overall structural behavior in various loadings. This study develops an artificial neural network (ANN) that predicts the ultimate interfacial bond strength (Pu) of circular and square CFSTs. The length of the interfacial bond between the tube and the concrete; the thickness, shape, and inner perimeter of the tube; and the cubic compression strength and age of the concrete are considered as model inputs. The modeling process uses 397 experimental datasets from 18 studies of push-out tests, more specifically, 143 square and 254 circular CFSTs. ANN with a hidden layer of error-propagation, feed-forward, and sigmoidal activation function is trained, tested, optimized, and validated to achieve a good estimate. The resulting model can predict the Pu with a satisfactory coefficient of determination (R2) of 0.87. The ability of the developed model to predict the Pu is compared to the proposed formulas in the literature. It is found that the ANN model provides the most accurate predictions among all suggested formulas, in terms of R2 and Taylor diagram analyses. Furthermore, the inclusion of the shape factor enabled the ANN model to predict the Pu of both squared and circular shapes of CFSTs. The validated ANN model is then used to examine the sensitivity of the parameters to the Pu. The correlations agreed well with the expected trend of published studies.