Prediction of Bond-Slip Behavior of Circular/SquaredConcrete-Filled Steel Tubes

Numerous existing formulas predicted the ultimate interfacial bond strength in concrete-filled steel tubes (CFST) between steel tubes and concrete core without investigating the wholeresponse under push-out load. In this research, four models are proposed to predict the interfacialbehavior in CFST including the post-peak branch under the push-out loading test based on 157 circularspecimens and 105 squared specimens from the literature. Two models (one for circular and one forsquared CFST) are developed and calibrated using artificial neural network (ANN) and two models(one for circular and one for squared CFST) are developed based on multivariable regression analysis,analysis of variance (ANOVA). The shape of the specimen (circular or squared), diameter of thetube, thickness of the tube, concrete compressive strength, age at the time of testing, and length ofthe specimen are the main factors considered. These models are then compared to other existingformulas to verify their capability to better predict the ultimate interfacial bond strength. It is foundthat the ANN model gives better results for most of the considered data. It is also found that ANNmodels can predict the overall bond-slip response for the considered dataset. In order to simulate theresponse of any CFST column using finite element (FE) method, it is vital to have sufficient inputdata on the overall bond-slip behavior between the interior face of the steel tube and the exteriorsurface of the concrete core including the post-peak branch. Accordingly, the suggested ANN modelis used to generate the required input data related to the cohesive behavior and damage along theinterface in ABAQUS model to simulate the response of two circular and two squared CFST columnsunder concentric compressive load. The results are in good agreement with experimental outcomes.The cohesive criterion and damage interface that are used based on ANN models in FE are found tobe sufficient and can be adopted to model CFST columns.