Abstract：
The existing computational method of static tensile bearing capacity of diaphragm-through joint of concrete-filled square steel tubular column has large errors comparing for the existing test results. In this paper, a prediction model for static tensile bearing capacity of diaphragm-through joint of concrete-filled square steel tubular column is developed based on BP neural network method. Predicted values are in good agreement with test results, which verifies the prediction accuracy and feasibility of neural network for predicting static tensile bearing capacity. Then, the parametric analysis for tensile bearing capacity of the joint is conducted using this neural network model, the specific parameters including: width-to-thickness ratio, diaphragm intensity, steel tube intensity, diaphragm thickness, diameter of concrete cast hole and concrete intensity. The parametric analysis indicates that width-to-thickness ratio, diaphragm intensity, steel tube intensity, diaphragm thickness and diameter of concrete cast hole have greater impact on static tensile bearing capacity, while concrete intensity has less impact on the capacity and it can be ignored.
MU You-zheng,RONG Bin,ZHANG Guang-tai et al. ANALYSIS ON STATIC TENSILE BEARING CAPACITY OF DIAPHRAGM-THROUGH JOINT OF CONCRETE-FILLED SQUARE STEEL TUBULAR COLUMN BASED ON NEURAL NETWORK[J]. Architecture Technology, 2015, 46(5): 459-462.
