隧道爆破近区振动的预测方法

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。

Predictionofblastingvibrationofareaneartunnelblastingsource

Theconceptionofscaleddistancewasappliedtotunnelblastingvibrationzoningandthe zoningwasjudgedbytheslopeofvelocityattenuationcurve.Basedontheresults,theBPwavelet neuralnetwork methodfortunnelblastingvibrationvelocityneartunnelblastingsourcewasproposed, thenthemethodwasusedintheQipanshantunnelstoverifythereliabilityofthemodel.CombinedwiththeengineeringpracticeofDapingshantunnels, newtunnelnexttoexistingtunnelblasting seismicwaveattenuationlawwasanalyzedandblastingvibrationvelocityneartunnelblastingsource waspredicted.Theresultsshowthat:(1)Thetunnelblastingvibrationzoningcanbejudgedby scaleddistanceasfollow:scaleddistance<5.0fortheareaneartunnelblastingsource;5.0≤scaled distance≤9.0fortheareabetweentheareaneartunnelblastingsourceandthearearemoteblasting source;scaleddistance>9.0forthearearemoteblastingsourcewithoutthemeasureddata.(2)BP waveletneuralnetworkcanbeusedtopredictthevelocitynearblastingsourceintheseparatedtunnel. Similarly,itcanalsobeusedtopredictthevelocityofnewtunnelnexttoexistingtunnel.The conclusionsofferreferencesfortunnelblastingconstructionundercomplexconditions.