摘要
将比例距离的概念应用于隧道爆破振动分区中,以速度衰减曲线斜率的大小作为分区计算的依据。在此基础上,提出采用BP小波神经网络的方法预测爆破近区振速,以棋盘山隧道实测数据验证模型的可行性。结合泉厦高速公路大坪山隧道工程实例,对临近既有隧道形式的隧道爆破地震波传播规律进行分析,并对近区振速进行预测。分析表明:(1)在无实测值时,隧道爆破分区可按比例距离大致划分为:比例距离<5.0为爆破近区;5.0≤比例距离≤9.0为爆破中区;比例距离>9.0为爆破远区。(2)BP小波神经网络爆破近区预测模型不仅适用于新建分离式隧道,也适用于临近既有隧道的新建小净距隧道。研究成果对复杂环境下的隧道钻爆施工具有一定的指导意义。
The conception of scaled distance was applied to tunnel blasting vibration zoning and the zoning was judged by the slope of velocity attenuation curve. Based on the results, the BP wavelet neural network method for tunnel blasting vibration velocity near tunnel blasting source was proposed, then the method was used in the Qipanshan tunnels to verify the reliability of the model. Combined with the engineering practice of Dapingshan tunnels, new tunnel next to existing tunnel blasting seismic wave attenuation law was analyzed and blasting vibration velocity near tunnel blasting source was predicted. The results show that. （1） The tunnel blasting vibration zoning can be judged by scaled distance as follow, scaled distance〈5.0 for the area near tunnel blasting source; 5.0≤scaled distance≤9.0 for the area between the area near tunnel blasting source and the area remote blasting source; scaled distance〉9.0 for the area remote blasting source without the measured data. （2） BP wavelet neural network can be used to predict the velocity near blasting source in the separated tun- nel. Similarly, it can also be used to predict the velocity of new tunnel next to existing tunnel. The conclusions offer references for tunnel blasting construction under complex conditions.
出处
《爆炸与冲击》
EI
CAS
CSCD
北大核心
2014年第3期367-372,共6页
Explosion and Shock Waves
基金
国家自然科学基金青年科学基金项目(51109084)
福建省自然科学基金项目(2014j01197)
福建省交通科技发展项目(200910)~~
关键词
爆炸力学
地震波传播规律
BP小波神经网络
爆破近区
预测
mechanics of explosion
seismic wave attenuation law
BP wavelet neural network
areanear blasting source
prediction
