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3D Slope Reliability Analysis Based on Improved PSO-RBF Neural Network
Peng Zonghuan, Sheng Jianlong, Ye Zuyang, Yuan Qianfeng
PDF(6275 KB)
PDF(6275 KB)
3D Slope Reliability Analysis Based on Improved PSO-RBF Neural Network
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Three-dimensional slope model can truly reflect the spatial effect of slope and improve the accuracy of slope reliability calculation, however, due to the huge calculation volume of three-dimensional slope model and the lack of display expression of safety coefficient, the slope reliability analysis is mainly based on two-dimensional simplified model, and the research for three-dimensional slope reliability analysis is still insufficient. A three-dimensional slope reliability analysis method based on Spencer’s method, adaptive variational particle swarm optimization algorithm and radial basis function neural network (RBF) is proposed. By introducing variational operators to the traditional PSO algorithm, the shortcomings of its low search accuracy and inefficient late iterations are improved. Based on the three-dimensional Spencer method, the calculation model of three-dimensional slope safety coefficient is constructed for reliability analysis by combining the improved PSO algorithm with RBF neural network to realize the display of three-dimensional slope function, and the improvement of the calculation accuracy and efficiency of the method compared with the traditional method is verified through the reliability analysis of the scalene vertebral ellipsoid slide; further research is conducted on the process of load-reducing excavation of the left bank slope of Kakiwa. The results show that the stability and reliability of the slope can be effectively improved after the effect of slope cutting and load reduction, and the probability of slope failure is reduced by nearly 2 orders of magnitude.
three-dimensional slope stabilization / reliability / particle swarm optimization algorithm / neural networks / geotechnical engineering / engineering geology
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