Prediction of peak particle velocity induced by underwater blasting based on the combination of grey relational analysis and genetic neural network

LIU Ya-qun; LI Hai-bo; PEI Qi-tao; ZHANG Wei

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Prediction of peak particle velocity induced by underwater blasting based on the combination of grey relational analysis and genetic neural network

更新时间：2026-06-25

- Prediction of peak particle velocity induced by underwater blasting based on the combination of grey relational analysis and genetic neural network
- Vol. 34, Issue S1, (2013)
- 作者机构：
  
  中国科学院武汉岩土力学研究所岩土力学与工程国家重点实验室,武汉,430071
- 作者简介：
- 基金信息：
- DOI：
  CLC： TV64
- Online First：09 June 2014，
  
  Published：2013
- 稿件说明：
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LIU Ya-qun, LI Hai-bo, PEI Qi-tao, et al. Prediction of peak particle velocity induced by underwater blasting based on the combination of grey relational analysis and genetic neural network[J]. 2013, 34(S1).
DOI：

LIU Ya-qun, LI Hai-bo, PEI Qi-tao, et al. Prediction of peak particle velocity induced by underwater blasting based on the combination of grey relational analysis and genetic neural network[J]. 2013, 34(S1). DOI：

摘要

水下爆破是一个复杂的、非线性的动态能量释放过程，其涉及到的影响因素众多。为了充分利用少量的实测数据，较准确地预测水下爆破质点峰值振动速度，引入灰色关联分析理论，并结合遗传神经网络较强的非线性映射优势和全局化的搜索能力，建立基于灰色关联分析的遗传神经网络模型(GRA-GA-BP)。该模型利用灰色关联分析理论，充分挖掘小样本潜在信息特征，较合理地确定了影响爆破振动速度的主要因素，解决了神经网络在多变量复杂系统中输入变量无法自动寻优的难题，从而增强了神经网络的适应能力和稳定性。采用该模型对广东台山核电站1期工程大襟岛水下爆破开挖质点峰值振动速度进行预测，并与传统的遗传神经网络及萨道夫斯基公式预测结果进行对比，发现GRA-GA-BP模型的预测值与实测值吻合更好，预测误差更稳定。研究方法可为小样本、多因素影响下类似工程质点峰值振动速度预测提供借鉴。

Abstract

Underwater blasting is a complicated

nonlinear

and dynamic process of energy release. It is affected by many factors

and its process has not been fully investigated at present. In order to accurately predict the peak particle velocity induced by underwater blasting based on a small amount of field measurements

the GRA-GA-BP model is established based on the grey relational analysis theory combining with the genetic neural network which has the nonlinear mapping and global searching capabilities. In the model

the potential information of the small sample is fully discovered

and the main factors affecting the vibration velocity are reasonably determined based on the grey relational analysis theory. Moreover

the problems of the neural network unable to automatically select and optimize input variables in complicated and multivariate systems are solved

which enhances the adaptability and stability of the genetic neural network. Finally

the GRA-GA-BP model is adopted to predict the peak particle velocity induced by underwater blasting at Dajin Island in the first phase of Taishan nuclear power station. Compared with the results obtained by traditional genetic neural network and the Sadaovsk formula

the prediction error of the GRA-GA-BP model is smaller and more stable. Therefore

the proposed procedure provides an appropriate way to predict the peak particle velocity induced by underwater blasting.

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