复线隧道掘进爆破振动监测与振速预测分析
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摘要
本文以新建太兴铁路太静段工程TXXS-1标段新悬泉寺隧道施工工程爆破振动安全监测项目为背景,将新建隧道爆破现场作为科研对象,主要开展以下几个方面的工作：
(1)对地震波类别及传播特征进行了阐述,计算得出爆破振动极限安全监测距离,为监测设备的布置提供了依据；介绍爆破开挖岩层应力场分布,根据此项目背景,说明既有线路处于新建隧道爆破施工的线弹性区。
(2)对新建隧道施工过程中的爆破振动进行了全程监测,获得了大量的监测数据,通过利用最小二乘法进行回归分析,确定经验公式中的k,a值,同时得出了新建隧道施工过程中爆破强度衰减规律。
(3)利用HHT分析方法,对信号进行分解,得到了爆破信号的信号组成及主要分量,并对主要分量进行包络,得到了新悬泉寺隧道施工过程中雷管的实际微差爆破延时时间,对工程爆破参数优化,具有重要参考价值。
(4)通过建立BP神经网络模型,实现了爆破振动强度三参量的准确预测,并对其在爆破振动强度预测中的极强的非线性处理能力进行了验证,通过与经验预测公式的对比表明：BP神经网络能够完全满足爆破振动强度的预测要求,预测精度高,预测过程简单,具有实际推广应用价值。
课题研究目标：
本篇论文主要采用在施工现场进行数据收集、辅助资料的调研、实验室理论分析相结合的方式进行研究。科学分析、合理优化和进行充分论证等各种研究方法之间相辅相成,目的在于克服工程施工难题的前提下获得科学有效的研究结论,从而在经济上有效控制工程投资,在施工工艺及方法上提高施工效率,加快施工进度,使新悬泉寺隧道尽快投入使用。
本文结合新悬泉寺隧道工程施工对既有线路的振动强度使用UBOX-5016动态振动测振仪进行现场监测,收集、整理数据并对其进行理论分析,了解新建隧道爆破对其小净距既有隧道的影响,从而选择合理的爆破参数,首先确保悬泉寺隧道的正常运营,再者保证新悬泉隧道安全、顺利的施工。依据施工现场爆破振动实测结果,使用最小二乘法和BP神经网络模型对爆破振动速度进行预测并比较得出预测方法的精准性
In this paper, it takes the TXXS-1section of new xuan quan temple tunnel construction blasting vibration safety monitoring project as the background, use the new tunnel blasting site as research object, Mainly conduct the following several aspects of the work:
(1)Expounds the category and propagation characteristics of seismic waves, calculated and get the blasting vibration limit safety monitoring distance. Provided a basis for the monitoring of equipment layout; Introduces rock stress field distribution of blasting excavation. According to the background of the project, descript the linear elastic region of blasting construction in existing line tunnel.
(2)The blasting vibration was monitored during the whole process of new tunnel construction blasting excavation, Access to a large number of monitoring data, through using the method of least squares regression analysis, determined the empirical formula K, a value. At the same time get the blasting strength decay laws in the new tunnel construction process.
(3)Use the HHT analysis method, process the signal decomposition, get the blasting signal constitution and main component, and main component of making an envelope, got a new suspended springs in the process of tunnel construction temple of the actual detonator differential blasting delay time, and the engineering blasting parameters optimization, have the important reference value.
(4)By establishing the BP nerve network model, realize the blasting vibration intensity of the three parameters accurate prediction, and in its blasting vibration intensity forecast in strong nonlinear processing power to the test and through the contrast of forecasting formula and experience shows that:the BP neural network can completely meet the requirement of the blasting vibration strength prediction, the forecast precision is high, the prediction process simple, practical application value.
Subject research goal:
This paper mainly used in construction field data collection, the auxiliary material research laboratory analysis theory, the way of the combination of the research. Scientific analysis, optimization and fully argument between various methods of study supplement each other, the purpose is to overcome project under the precondition of the construction problems for scientific and effective research conclusion, in economic and effective control of the project investment, in the construction technology and method to improve the construction efficiency, speed up the construction progress and make new suspension spring temple tunnel in use as soon as possible.
Combining with the new suspension of tunnel engineering construction springs temple both lines the vibration intensity UBOX-5016dynamic vibration use measuring instrument vibration monitoring, data collection, arrangement and the theoretical analysis, understand the new tunnel blasting on the small interval has the influence of the tunnel, and select rational blasting parameters, first make sure suspended springs temple of the normal operation of tunnel, and ensure that the new suspension spring tunnel safe, great construction. According to the construction site of the blasting vibration test results, using the least square method and the BP neural network model for prediction of blasting vibration velocity is concluded and forecast method precision.
(1)对地震波类别及传播特征进行了阐述,计算得出爆破振动极限安全监测距离,为监测设备的布置提供了依据；介绍爆破开挖岩层应力场分布,根据此项目背景,说明既有线路处于新建隧道爆破施工的线弹性区。
(2)对新建隧道施工过程中的爆破振动进行了全程监测,获得了大量的监测数据,通过利用最小二乘法进行回归分析,确定经验公式中的k,a值,同时得出了新建隧道施工过程中爆破强度衰减规律。
(3)利用HHT分析方法,对信号进行分解,得到了爆破信号的信号组成及主要分量,并对主要分量进行包络,得到了新悬泉寺隧道施工过程中雷管的实际微差爆破延时时间,对工程爆破参数优化,具有重要参考价值。
(4)通过建立BP神经网络模型,实现了爆破振动强度三参量的准确预测,并对其在爆破振动强度预测中的极强的非线性处理能力进行了验证,通过与经验预测公式的对比表明：BP神经网络能够完全满足爆破振动强度的预测要求,预测精度高,预测过程简单,具有实际推广应用价值。
课题研究目标：
本篇论文主要采用在施工现场进行数据收集、辅助资料的调研、实验室理论分析相结合的方式进行研究。科学分析、合理优化和进行充分论证等各种研究方法之间相辅相成,目的在于克服工程施工难题的前提下获得科学有效的研究结论,从而在经济上有效控制工程投资,在施工工艺及方法上提高施工效率,加快施工进度,使新悬泉寺隧道尽快投入使用。
本文结合新悬泉寺隧道工程施工对既有线路的振动强度使用UBOX-5016动态振动测振仪进行现场监测,收集、整理数据并对其进行理论分析,了解新建隧道爆破对其小净距既有隧道的影响,从而选择合理的爆破参数,首先确保悬泉寺隧道的正常运营,再者保证新悬泉隧道安全、顺利的施工。依据施工现场爆破振动实测结果,使用最小二乘法和BP神经网络模型对爆破振动速度进行预测并比较得出预测方法的精准性
In this paper, it takes the TXXS-1section of new xuan quan temple tunnel construction blasting vibration safety monitoring project as the background, use the new tunnel blasting site as research object, Mainly conduct the following several aspects of the work:
(1)Expounds the category and propagation characteristics of seismic waves, calculated and get the blasting vibration limit safety monitoring distance. Provided a basis for the monitoring of equipment layout; Introduces rock stress field distribution of blasting excavation. According to the background of the project, descript the linear elastic region of blasting construction in existing line tunnel.
(2)The blasting vibration was monitored during the whole process of new tunnel construction blasting excavation, Access to a large number of monitoring data, through using the method of least squares regression analysis, determined the empirical formula K, a value. At the same time get the blasting strength decay laws in the new tunnel construction process.
(3)Use the HHT analysis method, process the signal decomposition, get the blasting signal constitution and main component, and main component of making an envelope, got a new suspended springs in the process of tunnel construction temple of the actual detonator differential blasting delay time, and the engineering blasting parameters optimization, have the important reference value.
(4)By establishing the BP nerve network model, realize the blasting vibration intensity of the three parameters accurate prediction, and in its blasting vibration intensity forecast in strong nonlinear processing power to the test and through the contrast of forecasting formula and experience shows that:the BP neural network can completely meet the requirement of the blasting vibration strength prediction, the forecast precision is high, the prediction process simple, practical application value.
Subject research goal:
This paper mainly used in construction field data collection, the auxiliary material research laboratory analysis theory, the way of the combination of the research. Scientific analysis, optimization and fully argument between various methods of study supplement each other, the purpose is to overcome project under the precondition of the construction problems for scientific and effective research conclusion, in economic and effective control of the project investment, in the construction technology and method to improve the construction efficiency, speed up the construction progress and make new suspension spring temple tunnel in use as soon as possible.
Combining with the new suspension of tunnel engineering construction springs temple both lines the vibration intensity UBOX-5016dynamic vibration use measuring instrument vibration monitoring, data collection, arrangement and the theoretical analysis, understand the new tunnel blasting on the small interval has the influence of the tunnel, and select rational blasting parameters, first make sure suspended springs temple of the normal operation of tunnel, and ensure that the new suspension spring tunnel safe, great construction. According to the construction site of the blasting vibration test results, using the least square method and the BP neural network model for prediction of blasting vibration velocity is concluded and forecast method precision.
引文
[1]申玉生,高波,王志杰,孟凡君.复线隧道施工爆破对既有隧道结构的影响分析[J].地下空间与工程学报,2009,5(5)：981-982.
[2]黄宝龙.复杂坏境下的小间距隧道工程施工控制关键技术研究[D].北京：北京交通大学,2006.
[3]Carlos Lopez. Drilling and Blasting of Rock[M]. Printed In Netherlands.1995.
[4]毕继红,钟建辉.邻近隧道爆破振动对既有隧道影响的研究[J].工程爆破.2004,10,(4) ： 69-73.
[5]Stephen. D. Butt. Analysis of high frequenecy mieroseismicity recordedgeophsics at an underground hard rock mine [J]. Pure and appllied geophsies,1997 (150):693-704.
[6]Yu. T. R. New blast criteria for underground blasting[J]. CIM Bulletin.1996,89 (998): 139-145.
[7]李付胜.城市隧道掘进爆破数值模拟及振动效应控制研究[D].重庆：重庆大学,2008.
[8]陈建平,高文学,刘运通.岩体爆破结构效应研究[J].爆破,2005,22(2)：30-33.
[9]张志呈,肖正学,蒲传金等.岩石定向断裂控制爆破机理分析[J].石材,2006, (2)16-20.
[10]魏殿志.爆炸冲击波对煤体的变形和破坏作用分析[J].中国煤炭,2004,30(5)：41-42.
[11]杨军,金乾坤,黄凤雷.岩石爆破理论模型及数值计算[M].北京：科学出版社,1999.
[12]陈晨,张克绪,李伟.岩土工程施工技术[J].哈尔滨工业大学学报,2004,36(4)484-486.
[13]言之信.结构拆除及爆破振动效应研究[D].重庆.重庆大学博士论文.2002.
[14]王清涛.桩基应力波检测理论及工程应用[M].北京：地震出版社,1999.
[15]Yang Hyung-sik, Ju Jae-seong. On the Vibration Characteristics of Surface Blasting in Korea.The Second International Conference on Engineering Blasting Technique. Kunming, 1995.513-518.
[16]胡隶贤.地震工程学[M].北京：地震出版社,1988.
[17]李玉江,燕永峰,张秋华.关于爆破地震效应影响因素的研究[J].山西建筑,2010,36(30)： 71-72.
[18]凌同华,李夕兵,陈文胜.基于小波分析的爆破地震效应评估[J].振动与冲击,2007,26(2)： 124-127.
[19]凌同华,廖艳程,张胜.岩体爆破振动损伤评估的多元判别分析模型[J].中南大学学报(自然科学版),2010,41(1)：322-327.
[20]樊尚香,周浩敏.信息与测试技术[M].北京：北京航天航空大学出版社,2002：36-48.
[21]崔翔.信号分析与处理[M].北京：中国电力出版社,2005：32-48.
[22]史秀志,薛剑光,陈寿如.爆破振动信号双线性变换的二次型时频分析[J].振动与冲击,2008,27(12)：131-134.
[23]李鹏,卢文波,乔新明等.岩石高边坡开挖爆破振动信号处理与振动响应分析[J].煤炭学报,2011,36(增)：401-405.
[24]晏俊伟,龙,源,方向等.基于小波变换的爆破振动信号能量分布特征分析[J].爆炸与冲击,2007,27(5)：405-410.
[25]凌同华,李夕兵.爆破振动信号不同频带的能量分布规律[J].中南大学学报(自然科学版),2004,35(2)：310-315.
[26]王红兵.恒山隧道爆破施工技术探讨[J].山西建筑,2003,29(8)：240-241.
[27]刘金川,方向,冯伟涛.基于HHT方法的微差爆破延期时间识别与应用[J].山西建筑,201],37(4)：91-92.
[28]李宝山,张义平,王税睿.基于HHT方法在英坪矿n号坑的爆破振动分析[J].工程爆破,2010,16(2)：6-9.
[29]段生全,贺振华,黄德济.HHT方法及其在地震信号处理中的应用[J].成都理工大学学报(自然科学版),2005,32(4)：396-400.
[30]陶挺,林从谋,程李凯.爆破振动的BP神经网络预测方法研究[J].爆破,2011,28(4)： 106-107.
[31]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研究[J].爆破,2009,26(4)：4-5.
[32]李兆国.厦门海底隧道钻爆施工爆破振动规律研究[D].北京：北京交通大学,2009.
[33]罗伟,朱传云,祝启虎.隧洞光面爆破中炮孔堵塞长度的数值分析究[J].岩士力学,2008,29(9)：2487-2492.
[34]郝亚飞,李海波,刘恺德.单自由面爆破振动特征的炮孔堵塞长度效应[J].岩土力学,2011,32(10) ： 3105-3111.
[35]吴文平,冯夏庭,张传庆等.深埋硬岩隧洞围岩的破坏模式分类与调控策略[J].岩石力学与工程学报.2011,30(9)：1782-1802.
[36]张展宏.新建九燕山隧道对既有运营铁路隧道影响分析[D].成都：西南交通大学,2011.
[37]关继发.新建地铁隧道穿越既有地铁安全风险及其控制技术的研究[D].西安：西安建筑科技大学,2008.
[38]崔可佳.浅埋城市隧道爆破施工对地表及邻近既有隧道振动影响的研究[D].重庆：重庆大学,2008.
[39]孙芳强.新蛇皮沟隧道施工对既有隧道的稳定性影响研究[D].长安：长安大学硕士,2006.
[40]New B M. Ground Vibration Caused by Construction Works [J].Tunnel and undergroud space technology.1990,5 (3):32-39.
[41]张雪亮,黄树堂.爆破地震效应[M].北京：地震出版社.1981.
[2]黄宝龙.复杂坏境下的小间距隧道工程施工控制关键技术研究[D].北京：北京交通大学,2006.
[3]Carlos Lopez. Drilling and Blasting of Rock[M]. Printed In Netherlands.1995.
[4]毕继红,钟建辉.邻近隧道爆破振动对既有隧道影响的研究[J].工程爆破.2004,10,(4) ： 69-73.
[5]Stephen. D. Butt. Analysis of high frequenecy mieroseismicity recordedgeophsics at an underground hard rock mine [J]. Pure and appllied geophsies,1997 (150):693-704.
[6]Yu. T. R. New blast criteria for underground blasting[J]. CIM Bulletin.1996,89 (998): 139-145.
[7]李付胜.城市隧道掘进爆破数值模拟及振动效应控制研究[D].重庆：重庆大学,2008.
[8]陈建平,高文学,刘运通.岩体爆破结构效应研究[J].爆破,2005,22(2)：30-33.
[9]张志呈,肖正学,蒲传金等.岩石定向断裂控制爆破机理分析[J].石材,2006, (2)16-20.
[10]魏殿志.爆炸冲击波对煤体的变形和破坏作用分析[J].中国煤炭,2004,30(5)：41-42.
[11]杨军,金乾坤,黄凤雷.岩石爆破理论模型及数值计算[M].北京：科学出版社,1999.
[12]陈晨,张克绪,李伟.岩土工程施工技术[J].哈尔滨工业大学学报,2004,36(4)484-486.
[13]言之信.结构拆除及爆破振动效应研究[D].重庆.重庆大学博士论文.2002.
[14]王清涛.桩基应力波检测理论及工程应用[M].北京：地震出版社,1999.
[15]Yang Hyung-sik, Ju Jae-seong. On the Vibration Characteristics of Surface Blasting in Korea.The Second International Conference on Engineering Blasting Technique. Kunming, 1995.513-518.
[16]胡隶贤.地震工程学[M].北京：地震出版社,1988.
[17]李玉江,燕永峰,张秋华.关于爆破地震效应影响因素的研究[J].山西建筑,2010,36(30)： 71-72.
[18]凌同华,李夕兵,陈文胜.基于小波分析的爆破地震效应评估[J].振动与冲击,2007,26(2)： 124-127.
[19]凌同华,廖艳程,张胜.岩体爆破振动损伤评估的多元判别分析模型[J].中南大学学报(自然科学版),2010,41(1)：322-327.
[20]樊尚香,周浩敏.信息与测试技术[M].北京：北京航天航空大学出版社,2002：36-48.
[21]崔翔.信号分析与处理[M].北京：中国电力出版社,2005：32-48.
[22]史秀志,薛剑光,陈寿如.爆破振动信号双线性变换的二次型时频分析[J].振动与冲击,2008,27(12)：131-134.
[23]李鹏,卢文波,乔新明等.岩石高边坡开挖爆破振动信号处理与振动响应分析[J].煤炭学报,2011,36(增)：401-405.
[24]晏俊伟,龙,源,方向等.基于小波变换的爆破振动信号能量分布特征分析[J].爆炸与冲击,2007,27(5)：405-410.
[25]凌同华,李夕兵.爆破振动信号不同频带的能量分布规律[J].中南大学学报(自然科学版),2004,35(2)：310-315.
[26]王红兵.恒山隧道爆破施工技术探讨[J].山西建筑,2003,29(8)：240-241.
[27]刘金川,方向,冯伟涛.基于HHT方法的微差爆破延期时间识别与应用[J].山西建筑,201],37(4)：91-92.
[28]李宝山,张义平,王税睿.基于HHT方法在英坪矿n号坑的爆破振动分析[J].工程爆破,2010,16(2)：6-9.
[29]段生全,贺振华,黄德济.HHT方法及其在地震信号处理中的应用[J].成都理工大学学报(自然科学版),2005,32(4)：396-400.
[30]陶挺,林从谋,程李凯.爆破振动的BP神经网络预测方法研究[J].爆破,2011,28(4)： 106-107.
[31]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研究[J].爆破,2009,26(4)：4-5.
[32]李兆国.厦门海底隧道钻爆施工爆破振动规律研究[D].北京：北京交通大学,2009.
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[34]郝亚飞,李海波,刘恺德.单自由面爆破振动特征的炮孔堵塞长度效应[J].岩土力学,2011,32(10) ： 3105-3111.
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