摘要
针对隧道爆破振动信号时频解析度不足的难题,运用基于卷积神经网络的时频图像增强算法,增强实测隧道爆破信号时频图像,捕获到爆破信号能量在时频域上的聚集范围,从而重构得到反映爆破特征的真实信号;根据真实信号对爆破网络中雷管的起爆时刻进行了精确判别,识别隧道爆破雷管灾害源特征。分析表明:基于卷积神经网络的时频图像增强算法可有效抑制信号中的交叉项,最大限度地保留信号自有项,提高爆破信号能量聚集性和时频解析度;不同批次雷管混用是隧道安全的主要致灾因素,应加强监管以实现隧道安全高效施工。
Aiming at the problem of insufficient time-frequency resolution of tunnel blasting vibration signal,a time-frequency image enhancement algorithm based on convolutional neural network is applied,through the time-frequency image enhancement of the measured tunnel blasting signal,the aggregation range of the blasting signal energy in the time-frequency domain is captured,and the real signal reflecting the blasting characteristics is reconstructed;according to the real signal,the initiation time of detonator in blasting network is accurately distinguished,and the characteristics of tunnel blasting detonator disaster source are identified.The analysis shows that the time-frequency image enhancement algorithm based on convolutional neural network can effectively suppress the cross-terms in the signal,retain the auto-terms of the signal to the greatest extent,and improve the energy aggregation and time-frequency resolution of the blasting signal;The mixed use of different batches of detonators is the main disaster causing factor of tunnel safety.Supervision should be strengthened to realize safe and efficient tunnel construction.
作者
付晓强
麻岩
俞缙
戴良玉
黄凌君
Fu Xiaoqiang;Ma Yan;Yu Jin;Dai Liangyu;Huang Lingjun(College of Architecture and Civil Engineering,Sanming University,Sanming Fujian 365004,China;Fujian Sanming Yihong Construction Engineering Co.,Ltd.,Sanming Fujian 365499,China;Fujian Research Center for Tunneling and Urban Underground Space Engineering,Huaqiao University,Xiamen Fujian 361021,China;Sanming Coffer Fine Chemical Industrial Co.,Ltd.,Sanming Fujian 365500,China)
出处
《矿业科学学报》
CSCD
2023年第3期348-356,共9页
Journal of Mining Science and Technology
基金
福建省自然科学基金(2020J01390)
三明学院国家基金培育计划(PYT2008)
国家自然科学基金(51874144,51679093)。
关键词
隧道爆破
时频谱
灾害源
特征提取
爆破安全
tunnel blasting
time frequency spectrum
disaster source
feature extraction
blasting safety
