Study on Intelligent Prediction Method of Peak Ground Acceleration Amplification Coefficient of Accumulation Type Slope Based on Shaking Table Test

被引：0
|
作者：
Wang X. [1 ]
Li Z. [2 ]
Wang D. [3 ]
Guo X. [2 ]
Yang C. [2 ]
Pei X. [4 ]
机构：
[1] School of Civil Engineering, Beijing Jiaotong University, Beijing
[2] School of Civil Engineering, Southwest Jiaotong University, Chengdu
[3] China Railway Eryuan Engineering Group Co. Ltd., Chengdu
[4] State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu
来源：
Tiedao Xuebao/Journal of the China Railway Society | 2022年 / 44卷 / 06期
关键词：
PGA amplification coefficient; Random reconnection BP neural network; Rock slope; Shaking table model test;
D O I：
10.3969/j.issn.1001-8360.2022.06.014
中图分类号：
学科分类号：
摘要：
Earthquake peak acceleration (PGA) amplification coefficient is one of the important parameters to study the action of the earthquake. A total of 150 groups of large-scale shaking table tests of accumulation slope for accumulation-type slopes that can consider the influence of seismic wave types and slope shape were designed and completed. The acceleration amplification effect of accumulation slope was systematically analyzed. By improving the traditional three-layer feed forward BP neural network model, a random rewiring BP neural network model was put forward. On this basis, considering the influence of various factors, such as slope parameters, parameters of seismic wave and soil parameters, an intelligent prediction model of peak acceleration amplification of accumulation slope based on random rewiring BP neural network was established. With the help of model test and robustness analysis, the calculation precision of the model was verified and the stability analysis was carried out. The results show that the model, with good stability, meets the target requirements in calculation accuracy, which makes up for the defects of the traditional single function calculation method, and can provide direct reference for the determination of seismic intensity in the seismic design of tunnel engineering, bridge engineering and slope engineering of high-intensity mountain railways, which is conducive to the development of the intelligent geotechnical engineering. © 2022, Department of Journal of the China Railway Society. All right reserved.
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页码：116 / 122
页数：6
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