Punching shear failure of slab-column connections can cause the progressive collapse of a structure. In this study, a punching test database is first established. Then, based on the Levenberg–Marquardt (LM) algorithm and using the nonlinear function of the backpropagation neural network (BPNN), a prediction model of the punching capacity of slab-column connections without transverse reinforcement is established. Finally, the proposed model is compared with the formulas of the Chinese, American, and European standards using several methods. The statistical eigenvalue method shows that the BPNN model has the highest accuracy and the lowest dispersion. The defect point counting method shows that the BPNN model had the fewest total number of defects and was the safest and most economical. The influencing factor analysis suggests that factors in the BPNN model had the most reasonable influence on the punching bearing capacity of slab-column connections. Finally, the model is verified using a case study and the Matlab program. The results show that the average error of the formulas in the Chinese, American, and European standards are 21.08%, 30.21%, and 11.47%, respectively, higher than that of the BPNN model.
Abstract. The progressive collapse of structures under accidental actions is a serious threat to the public safety. Numerical analysis of a 2x2-bay and single-story reinforced concrete flat plate frame model is done in this paper, the time of failure and the position of failure column are taken into account. It can be found that the Dynamic Amplification Factor (DAF) increases with the decrease of failure time, the DAF under compressive membrane action is less than the stage of the tensile membrane action and the compressive membrane action is more important to avoid progressive collapse. When the time of failure is not changed, the DAF is negatively correlated with the load under compressive membrane action and the DAF is positively correlated with the load under tensile membrane action.
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