爆破振动特征参量的BP小波预测
|
摘要
以福建泉州南惠高速公路NH5标段路基爆破开挖工程为实例,运用人工神经网络原理,以孔径、孔深、孔距、排距、最大单孔药量、单段最大药量、总药量和爆源距离作为影响爆破振动的主要因素,建立BP小波神经网络模型.对质点的水平径向、水平切向、垂直方向等3个方向分别预测其爆破振动速度峰值及频率,并将预测结果与BP神经网络、支持向量机的预测结果进行对比.实验结果表明:BP小波神经网络的爆破振动速度峰值-频率模型预测收敛快、精度高,优于标准BP网络和支持向量机模型,其结果更加符合国家标准GB 6722-2003《爆破安全规程》的评价要求.
Taking the Nanhui expressway NH5 section′s subgrade blasting excavation project in Quanzhou,Fujian as example,adopting artificial neural network theory,the BP wavelet neural network model was established,which considers various main factors,such as the charge hole diameter,distance,and depth,column distance between charge holes,line maximum charge of single hole,maximum charge weight per delay interval,total charge and explosive distance.By the BP wavelet neural network model,the blasting vibration peak value and main frequency were predicted in three directions separately,namely horizontal radial,horizontal tangential and vertical.The prediction results were compared with BP neural network and support vector machine model.The results show that: BP wavelet neural network model of blasting vibration peak value and main frequency owns fast convergence and high precision,so BP wavelet neural network mode is better than BP neural network model and support vector machine model,it meets well the requirements of "Demolition Safety Regulation"(GB 6722-2003).
Taking the Nanhui expressway NH5 section′s subgrade blasting excavation project in Quanzhou,Fujian as example,adopting artificial neural network theory,the BP wavelet neural network model was established,which considers various main factors,such as the charge hole diameter,distance,and depth,column distance between charge holes,line maximum charge of single hole,maximum charge weight per delay interval,total charge and explosive distance.By the BP wavelet neural network model,the blasting vibration peak value and main frequency were predicted in three directions separately,namely horizontal radial,horizontal tangential and vertical.The prediction results were compared with BP neural network and support vector machine model.The results show that: BP wavelet neural network model of blasting vibration peak value and main frequency owns fast convergence and high precision,so BP wavelet neural network mode is better than BP neural network model and support vector machine model,it meets well the requirements of "Demolition Safety Regulation"(GB 6722-2003).
引文
[1]闫鸿浩,李晓杰,曲艳东,等.爆破振动速度测试精细分析[J].岩土力学,2007,28(10):2091-2094.
[2]毕卫国,石崇.爆破振动速度衰减公式的优化选择[J].岩土力学,2004,25(9):99-102.
[3]MOSTAFA T M.Artificial neural network for prediction and control of blasting vibrations in Assiut(Egypt)lime-stone quarry[J].International Journal of Rock Mechanics and Mining Sciences,2009,46(2):426-431.
[4]唐海,石永强,李海波,等.基于神经网络的爆破振动速度峰值预报[J].岩石力学与工程学报,2007,26(增刊1):3533-3539.
[5]林丽群,林从谋,蔡丽光,等.隧道爆破振动的BP神经网络预测及应用[J].爆破,2009,26(4):4-7.
[6]MANOJ K.Evaluation and prediction of blast induced ground vibration using support vector machine[J].MiningSciences and lechnology,2010(1):64-70.
[7]史秀志,董凯程,邱贤阳,等.基于支持向量机回归爆破振动速度预测分析[J].工程爆破,2009,15(3):28-30.
[8]JAVIER T,RAFAEL R.FEM models including randomness and its application to the blasting vibrations prediction[J].Computers and Geotechnics,2006,33(1):15-28.
[9]宗琦,汪海波,周胜兵.爆破地震效应的监测和控制技术研究[J].岩石力学与工程学报,2008,27(5):938-945.
[10]孟海利,郭峰.爆破地震波主频率的试验研究[J].铁道工程学报,2009(11):81-83.
[11]SINGH T N,VIRENDRA S.An intelligent approach to prediction and control ground vibration in mines[J].Geotechnical and Geological Engineering,2005,23(3):249-262.
[12]李洪涛,舒大强.爆破震动衰减规律的影响因素[J].武汉大学学报:工学版,2005,38(1):79-82.
[13]许红涛,卢文波.几种爆破震动安全判据[J].爆破,2002,19(1):8-10.
[14]黄志波,林从谋,黄金山,等.BP小波神经网络在大断面隧道变形预测中的应用[J].华侨大学学报:自然科学版,2011,32(11):680-683.
[15]李金屏,王风涛,杨波.BP小波神经网络快速学习算法研究[J].系统工程与电子技术,2001,23(8):72-74.
[16]林键,林从谋,林丽群.爆破振动荷载作用下3～4层房屋结构响应测试研究[J].振动与冲击,2010,29(3):48-51.
[2]毕卫国,石崇.爆破振动速度衰减公式的优化选择[J].岩土力学,2004,25(9):99-102.
[3]MOSTAFA T M.Artificial neural network for prediction and control of blasting vibrations in Assiut(Egypt)lime-stone quarry[J].International Journal of Rock Mechanics and Mining Sciences,2009,46(2):426-431.
[4]唐海,石永强,李海波,等.基于神经网络的爆破振动速度峰值预报[J].岩石力学与工程学报,2007,26(增刊1):3533-3539.
[5]林丽群,林从谋,蔡丽光,等.隧道爆破振动的BP神经网络预测及应用[J].爆破,2009,26(4):4-7.
[6]MANOJ K.Evaluation and prediction of blast induced ground vibration using support vector machine[J].MiningSciences and lechnology,2010(1):64-70.
[7]史秀志,董凯程,邱贤阳,等.基于支持向量机回归爆破振动速度预测分析[J].工程爆破,2009,15(3):28-30.
[8]JAVIER T,RAFAEL R.FEM models including randomness and its application to the blasting vibrations prediction[J].Computers and Geotechnics,2006,33(1):15-28.
[9]宗琦,汪海波,周胜兵.爆破地震效应的监测和控制技术研究[J].岩石力学与工程学报,2008,27(5):938-945.
[10]孟海利,郭峰.爆破地震波主频率的试验研究[J].铁道工程学报,2009(11):81-83.
[11]SINGH T N,VIRENDRA S.An intelligent approach to prediction and control ground vibration in mines[J].Geotechnical and Geological Engineering,2005,23(3):249-262.
[12]李洪涛,舒大强.爆破震动衰减规律的影响因素[J].武汉大学学报:工学版,2005,38(1):79-82.
[13]许红涛,卢文波.几种爆破震动安全判据[J].爆破,2002,19(1):8-10.
[14]黄志波,林从谋,黄金山,等.BP小波神经网络在大断面隧道变形预测中的应用[J].华侨大学学报:自然科学版,2011,32(11):680-683.
[15]李金屏,王风涛,杨波.BP小波神经网络快速学习算法研究[J].系统工程与电子技术,2001,23(8):72-74.
[16]林键,林从谋,林丽群.爆破振动荷载作用下3～4层房屋结构响应测试研究[J].振动与冲击,2010,29(3):48-51.
版权所有：© 2023 中国地质图书馆 中国地质调查局地学文献中心