运营地铁隧道自动化监测数据的修正及精度分析

发布时间：2018-04-26 08:05

本文选题：自动化监测 + 静力水准仪　；参考：《成都理工大学》2017年硕士论文

【摘要】：随着经济的发展和城市化的推进,越来越多的人涌入了城市。过大的人口密度给地面交通造成了巨大压力,交通拥堵成为制约城市发展的一大顽疾。为了减轻地面交通压力,许多大城市把发展地下轨道交通作为解决之道。随着城市的建设发展,地铁隧道附近出现了越来越多的高层建筑,可能对隧道的安全性和稳定性造成严重的影响,从而危及整个城市的交通系统。为确保运营地铁隧道的结构安全,保障地铁线路的安全运行,确保广大乘客的人身安全,对运营地铁隧道进行安全监测具有重大意义。传统的地铁隧道监测方法,由于成本、精度、时效、可行性等因素,对比于新兴的监测手段,已经逐渐失去优势。在运营地铁隧道的监测中,为及时了解地铁隧道变形情况,实现信息化监测,保证地铁隧道的绝对安全,以及对突发状况及时做出反应,一般采取人工监测与自动化监测相结合的作业模式。在运营地铁隧道自动化监测中使用的静力水准仪和激光测距仪等监测仪器进行隧道沉降与收敛两项监测指标的测量。此种仪器具有高分辨率、高精度、高稳定性、高可靠性、响应时间快,工作寿命长等优点;所有自动化数据通过无线网络传输至数据采集监控平台,通过网页形式发布。用户可以对项目实时进行管理、维护等操作。大量经验表明,运营地铁隧道自动化监测系统具有良好的精度,极佳的时效性,合理的成本等优点,已成为现在运营地铁隧道监测的主要手段。但在实践中也发现在隧道收敛较大的区间段,沉降自动化监测的数据与人工测量的数据有较大偏差;而在收敛较小的区间段,沉降自动化监测的数据与人工测量数据又比较吻合。所以,本文试图从地铁隧道的数学几何模型和监测数据的数理统计模型入手,根据造成沉降数据不一致的原因,提出修正模型,对运行地铁隧道自动化监测数据进行修正,使地铁沉降自动化监测数据达到更高的精度。结果表明,两种修正模型都可以提高沉降自动化监测数据的精度。其中几何修正模型的适应性最好,局部修正后的数据精度虽然也能满足要求,但是不如回归模型的修正精度高。回归模型的局部修正精度虽然很高,但是适用性较差。通过本文研究,不仅可以提高地铁隧道沉降自动化数据的精度,而且还能提升地铁隧道自动化监测系统的稳定性,适用性,对完善数据处理流程,拓展系统功能,有着很大意义。
[Abstract]:With the development of economy and urbanization, more and more people pour into the city. The excessive population density has brought great pressure to the ground traffic, and traffic congestion has become a serious problem restricting the development of the city. In order to reduce the ground traffic pressure, many big cities take the development of underground rail transit as the solution. With the development of urban construction, more and more high-rise buildings appear near the subway tunnel, which may have a serious impact on the safety and stability of the tunnel, thus endangering the traffic system of the whole city. In order to ensure the structural safety of the subway tunnel, ensure the safe operation of the subway line, and ensure the safety of the passengers, it is of great significance to monitor the safety of the operating subway tunnel. The traditional subway tunnel monitoring method has gradually lost its advantage compared with the new monitoring methods because of the factors of cost, precision, aging, feasibility and so on. In the monitoring of subway tunnel operation, in order to understand the deformation of subway tunnel in time, realize information monitoring, ensure the absolute safety of subway tunnel, and make timely response to the unexpected situation, In general, manual monitoring and automatic monitoring combined with the operation model. The static level and laser rangefinder used in the automatic monitoring of subway tunnel are used to measure the settlement and convergence of the tunnel. This instrument has the advantages of high resolution, high precision, high stability, high reliability, fast response time, long working life, etc. All the automatic data are transmitted to the data acquisition and monitoring platform through wireless network and published through the form of web pages. Users can manage and maintain the project in real time. A great deal of experience shows that the automatic monitoring system of subway tunnel has the advantages of good precision, excellent timeliness, reasonable cost and so on, which has become the main means of subway tunnel monitoring. However, in practice, it is also found that in the section where the tunnel converges greatly, there is a big deviation between the data of automatic monitoring of settlement and that of manual measurement, while in the section where the convergence is small, The data of automatic settlement monitoring are in good agreement with the data of manual measurement. Therefore, this paper attempts to start with the mathematical geometry model of subway tunnel and the mathematical statistical model of monitoring data. According to the causes of the inconsistency of settlement data, this paper puts forward a modified model to correct the automatic monitoring data of running subway tunnel. The automatic monitoring data of subway settlement can achieve higher accuracy. The results show that both modified models can improve the accuracy of automatic monitoring data. The geometric correction model has the best adaptability, and the data precision after local correction can meet the requirements, but it is less accurate than the regression model. Although the local correction accuracy of the regression model is very high, the applicability of the regression model is poor. Through the research of this paper, not only the precision of the automatic data of subway tunnel settlement can be improved, but also the stability and applicability of the automatic monitoring system of subway tunnel can be improved, and the data processing flow can be improved and the system function can be expanded. It means a lot.
【学位授予单位】：成都理工大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：U456

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