Research on axial bearing capacity of rectangular concrete-filled steel tubular columns based on artificial neural networks

Design of rectangular concrete-filled steel tubular （CFT） columns has been a big concern owing to their complex constraint mechanism. Generally, most existing methods are based on simplified mechanical model with limited experimental data, which is not reliable under many conditions, e.g., columns using high strength materials. Artificial neural network （ANN） models have shown the effectiveness to solve complex problems in many areas of civil engineering in recent years. In this paper, ANN models were employed to predict the axial bearing capacity of rectangular CFT columns based on the experimental data. 305 experimental data from articles were collected, and 275 experimental samples were chosen to train the ANN models while 30 experimental samples were used for testing. Based on the comparison among different models, artificial neural network modell （ANN1） and artificial neural network model2 （ANN2） with a 20- neuron hidden layer were chosen as the fit prediction models. ANN1 has five inputs： the length （D） and width （B） of cross section, the thickness of steel （t）, the yield strength of steel （fy）, the cylinder strength of concrete （fc＇）- ANN2 has ten inputs： D, B, t, fy, f′, the length to width ratio （D/B）, the length to thickness ratio （D/t）, the width to thickness ratio （B/t）, restraint coefficient （ξ）, the steel ratio （α）. The axial beating capacity is the output data for both models.The outputs from ANN1 and ANN2 were verified and compared with those from EC4, ACI, GJB4142 and AISC360-10. The results show that the implemented models have good prediction and generalization capacity. Parametric study was conducted using ANN1 and ANN2 which indicates that effect law of basic parameters of columns on the axial bearing capacity of rectangular CFT columns differs from design codes.The results also provide convincing design reference to rectangular CFT columns.

Test and damage assessment of shallow buried RC tunnel under explosion

As a major element of the transportation network,tunnels are unavoidably threatened by accidental loads such as vehicle bombs and tank truck explosions.The goal of this research is to explore the dynamic characteristics and damage assessment of tunnel structures under contact blast loads.First,three scaled-down reinforced concrete tunnel models were made,and the explosion test and static loading test were carried out successively to evaluate the axial residual bearing capacity,axial displacement and failure mechanism of the tunnel.Secondly,the finite element model is built by utilizing LS-DYNA,and the reliability of the finite element method is confirmed by comparing the data of the explosion test with the static loading test.At the same time,the calculation method for damage coefficient and the classification criteria for damage grade based on axial residual bearing capacity are presented.Then,based on the finite element method,the propagation process of the explosion shock wave in the tunnel and the damage mechanism of the tunnel are investigated.Finally,seven explosion scenarios are developed,the damage degree of these seven tunnels under the blast load is quantitatively analyzed,and further anti-blast design ideas are put forth.The study in this article may give an intended reference for the damage assessment,anti-explosion design and strengthening work of reinforced concrete tunnels.