【作者】 崔巍；

【导师】 张世平；

【作者基本信息】 太原理工大学 ， 采矿工程， 2012， 硕士

【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更多还原

【Abstract】 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.更多还原