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中文摘要：
以广东岭澳核电站二期工程20 m平台和核岛爆破开挖为实例,运用人工神经网络原理,以孔径、孔深、孔距、排距、最小抵抗线、最大单孔药量、最大段药量、堵塞长度、总药量、高程差和爆源距作为影响爆破振动速度的主要因素,建立BP神经网络模型,对质点爆破振动速度峰值进行预测。分析结果表明,运用提出的神经网络预测模型精确度明显高于传统的萨道夫斯基公式。
英文摘要：
Because of the influence of charge parameters and rock properties,it is difficult to accurately predicate the vibration characteristics in the engineering blasting.Based on the 20 meters platform and nuclear island blasting excavation monitoring in the second phase of Linga′o nuclear power station,Guangdong Province,the artificial neural network is adopted to predict the peak velocity of blasting vibration.In the analysis,the charge hole diameter,distance,and depth,column distance between charge holes,line of least resistance,maximum charge of single hole,maximum charge weight per delay interval,clogging depth of hole,total charge,magnitude of relative altitude and explosive distance are considered to establish the back-propagation neural network model.The prediction results through artificial neural network are more accurate than those of Sadaovsk formula.
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