Rockburst Prediction Model Based on Entropy Weight Integrated with Grey Relational BP Neural Network

被引：24

作者：

Zheng, Yuchao ^{[1
]}

Zhong, Heng ^{[1
]}

Fang, Yong ^{[1
]}

Zhang, Wensheng ^{[1
]}

Liu, Kai ^{[1
]}

Fang, Jing ^{[1
]}

机构：

[1] Southwest Jiaotong Univ, Sch Civil Engn, Key Lab Transportat Tunnel Engn, Minist Educ, Chengdu, Sichuan, Peoples R China

来源：

ADVANCES IN CIVIL ENGINEERING | 2019年 / 2019卷

基金：

中国国家自然科学基金;

关键词：

HIGHWAY TUNNEL; ROCK; ACCIDENTS;

D O I：

10.1155/2019/3453614

中图分类号：

TU [建筑科学];

学科分类号：

0813 ;

摘要：

A rockburst prediction model of the entropy weight grey relational backpropagation (BP) neural network is developed. The model needs to select the evaluation factors according to the engineering practice and establish the sample library. The entropy weight method is used to calculate the objective weight of the characteristic factors, and the similarity between the samples is calculated by the combination of grey relational theory and the entropy method. The training sample of the BP neural network is selected by threshold determination. Finally, we use the trained neural network to estimate the rockburst intensity grade of samples to be tested. This model is applied to the rockburst prediction of Qamchiq tunnel project, and the prediction results are in good agreement with the actual conditions of the subsequent construction, thus verifying the feasibility and effectiveness of the model in the rockburst prediction.

引用

页数：8

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