Prediction of blasting vibration velocity peak based on an improved PSO - BP neural network

被引：0

作者：

Fan Y. ^{[1
]}

Pei Y. ^{[1
]}

Yang G. ^{[1
]}

Leng Z. ^{[1
,2
]}

Lu W. ^{[3
]}

机构：

[1] Hubei Key Laboratory of Construction and Management in Hydropower Engineering, China Three Gorges University, Yichang

[2] Chongqing Engineering Technology Research Center of Industrial Explosive Materials, China Gezhouba Group Explosive Co.，Ltd., Chongqing

[3] Key Laboratory of Rock Mechanics in Hydraulic Structural Engineering ,Ministry of Education, Wuhan University, Wuhan

来源：

Zhendong yu Chongji/Journal of Vibration and Shock | 2022年 / 41卷 / 16期

关键词：

back propagation ( BP) neural network; blasting vibration; longitudinal wave velocity; particle swarm optimization (PSO) algorithm; peak blasting vibration velocity;

D O I：

10.13465/j.cnki.jvs.2022.16.025

中图分类号：

学科分类号：

摘要：

In order to improve the accuracy of blasting vibration velocity peak prediction，the ability of back propagation ( BP) neural network to solve complex nonlinear function approximation and the global optimization ability of particle swarm optimization (PSO) are combined, establishment an improved PSO - BP neural network prediction model, using the improved PSO to optimize the initial weight and threshold of a BP neural network. Based on blasting excavation monitoring data of dam abutment on left bank of Baihetan hydropower station，selecting blast center distance，maximum charge per delay, height difference and longitudinal wave velocity as input parameters, the strengths of the relations between the input parameters and the peak value of blasting vibration velocity were analyzed by the cosine amplitude method. It can be concluded that the longitudinal wave velocity representing the site conditions is also an important factor affecting the propagation of blasting vibration velocity. Compared with the test results of the BP neural network and the Sadovsky formula, results show that the prediction value of the improved PSO - BP neural network prediction model is better consistent with the measured value, the results are more reliable, and the model has good generalization ability. The proposed method provides a reference for the prediction of blasting vibration velocity peak in similar projects. © 2022 Chinese Vibration Engineering Society. All rights reserved.

引用

页码：194 / 203and302

共 21 条

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