穿越工程影响下既有地铁隧道变形监测与分析
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摘要
在穿越工程施工过程中,对既有地铁隧道实施变形监测是确保地铁运营安全的重要措施之一。本文针对地铁运营环境和结构变形特征,研究穿越工程影响下地铁隧道及轨道结构变形监测和分析的理论、方法和技术。以“监测、辨险、预警”为主线,通过关键理论与技术的研究,实现对既有地铁隧道受近接施工扰动形变全过程的精密监控,达到“监测于变形”、“辨险于分析”、“预警于未然”,保障地铁的安全运营。
论文在总结传统地铁隧道变形监测方法及其问题的基础上,对精密监测基准、智能变形预测、特征点全自动监测、地铁变形区整体监测等方面的理论和方法开展了深入的研究,解决了如何在变形复杂、环境干扰的地铁运营条件中实施准确全面监测的难题。主要研究内容与创新如下：
1.结合穿越地铁工程实践,总结出运营地铁隧道受近接施工扰动变形的主要影响因素,以及监测数据呈现非线性和混沌特性,需要高精度监测等主要特点。在此基础上,重点研究了建立高精度三维监测基准的理论,构建了加权七参数空间三维坐标转换模型,并推导了精密测量定权和平差的数学模型。
2.鉴于地铁监测基准点随着城市地面整体沉降的问题,研究出应用GPS动态监测改正地铁基准点沉降的方法。利用高频GPS坐标时间序列中噪声的自相关性构建了噪声自相关函数,提出基于分形算法的Kalman滤波消噪模型,实现GPS沉降变形的提取与基准点坐标的沉降改正。
3.根据地铁监测基准需要高精度和实时动态的特点,研制了专用的测量标志,使激光跟踪仪、工业全站仪、GPS、地面三维激光扫描仪、测量机器人等多种高精尖仪器测量坐标一体化,实现了精密基准和GPS空间动态基准的统一,从而构建了地铁时空一体化精密监测基准。
4.对当前地铁监测数据量大、变形分析困难、预测准确率低的技术难题,根据地铁受近接施工扰动变形数据的非线性与混沌特性,提出基于PBIL支持向量机的智能变形预测方法。用基于概率分析的PBIL算法对支持向量机的关键参数组合(C,σ,ε)进行设定。并根据变形方向和变形速度体现变形机理的特点,融合了粒子群算法中粒子飞行兼顾个体增益和群体增益的方向和速度的更新机制,精确设定最优参数组合。从而避免人为选择参数的盲目性,极大地提高了支持向量机的预测能力。通过广州地铁穿越工程变形监测数据预测的应用验证了该变形分析方法具有高精度预测能力和实用性。
5.系统总结了用测量机器人对地铁变形特征点实施全自动监测的理论和方法。鉴于监测中存在地铁运营扰动的问题,研究了基于微动改正模型的自动监测技术。研发了测量机器人地铁全自动监测系统,实现了测量机器人监测行为的自动控制、监测数据的管理分析与智能预测。
6.为实现提高辨识危险隐患能力、全面监测地铁变形的目的,提出了利用三维激光扫描技术进行地铁三维全景监测模式及基于NURBS曲面的点云数据变形提取模型,并用工程实验检验了模型的实效性。鉴于地铁监测的实时特点,在监测区域大或高采样率导致点云处理与变形提取的时间开销很大的情况下,研究出一种基于切片拟合技术的地铁隧道变形快速量测方法,可以用最短的时间确定变形超限的区域。
论文的研究成果在北京地铁1号线、10号线、6号线,广州地铁2号线和深圳地铁1号线等既有地铁变形监测工程中得到测试、验证和应用。
During the traversing engineering, to monitor the existing subway tunnel deformation real-time is an important part of ensuring the safety of subway operation.The subway operation characteristics and the environment are considered the relevant theoretical approaches and key technologies about the subway tunnel structure deformation monitoring and deformation analysis are proposed where the subway tunnel is disturbed by the adjacent construction. Monitoring, identified risks and warning are the main research line. Though the key technology research subject the whole process of security control to the existing subway tunnel construction disturbance deformation is fulfilled. As a result we are successful in monitoring in the deformation, identifying risks on the analysis of key technologies, and early warning in the first place, which ensures the safe operation of the subway.
The paper systematically summarizes the operating subway deformation theory and problems of monitoring and makes in-depth research on the benchmarks of precise monitoring, intelligent deformation prediction, automatic monitoring of feature points, subway deformation zone whole monitoring theory and methods. The research kernel and innovation of the paper are as follows.
1. With the combination the traversing subway projects, the paper summed up that the disturbance deformation of operating subway caused by the adjacent construction is influenced by many factors, the monitoring data shows nonlinear and chaotic characteristics and it needs high-precision monitoring. On this basis, the paper focuses on the theory of building three-dimensional high-precision monitoring benchmark, and weighted seven-parameter space three-dimensional coordinate transformation model is constructed, and the mathematical model of precision monitoring the weight and adjustment is derived
2. In view of the status that the subway monitoring reference point will have settlement with the whole urban ground subsidence the paper proposed the method to correct subway reference point settlement with the application of GPS dynamic monitoring. It constructs noise autocorrelation function with the use of noise autocorrelation in the high-frequency GPS coordinate time series, puts forward the Kalman filtering denoising model based on the fractal algorithm, and fulfills GPS subsidence deformation extract and the reference point coordinates settlement corrections.
3. Because the subway benchmarks for monitoring needs high precision and real-time dynamic characteristics, a dedicated survey marks is developed. The coordinates of many sophisticated measure instruments are integrated such as laser tracking device, industrial total station, GPS, terrestrial3D laser scanner, measuring robots and so forth. A precision reference and the unity of the GPS space on a dynamic basis is achieved, and as a result the subway precise spatial and temporal integration benchmarks for monitoring is built.
4. According to technical problems such as the subway monitoring data on the current is huge and the deformation analysis is difficult and the prediction has low accuracy, and according to the subway deformation data disturbed by adjacent construction is nonlinear and chaotic characteristics, the paper proposes the intelligent deformation prediction method based on the PBIL support vector machine (SVM). PBIL algorithm based on probability analysis is used to obtain the key parameters of support vector machine combinations (C,a,s). And in accordance with the characteristics of the deformation mechanism of the deformation direction and deformation rate, the particle swarm optimization (PSO) algorithm is introduced. Its update mechanism of both individual gain and groups'gain of the particle swarm optimization in particle flight direction and speed is used to guide the setting of optimal parameter combinations. Consequently the blindness of human choice of parameters is avoided and support vector machine prediction ability is improved. The deformation monitoring data of Guangzhou subway traversing project verifies that the deformation analysis has high accuracy predictive ability and practicability.
5. The paper summarizes the theories and methods of using automatic monitoring measuring robot to the implementation the subway deformation characteristics points. View of the problem of disturbance in the subway monitoring, the principle of the automatic monitoring of the model based on micro corrections is put forward. Measuring robot subway automatic monitoring system is developed to achieve the automatic control of monitoring behavior, data management and intelligent forecast of monitoring data.
6. To improve the identification of the threats and achieve the capability to fully monitoring subway deformation,3-D panoramic monitoring mode with the use of3D laser scanner is proposed. The panoramic three-dimensional monitoring station layout and scanning, the point cloud data processing principle are researched. The innovative point is the point cloud data deformation extraction model based on the NURBS surface, and engineering experiments are used to test the effectiveness of the model of deformation and extraction. View of the real-time characteristics of subway monitoring, either the large monitoring regional or the higher sampling rate will result in the huge time spending of point cloud processing and deformation extraction, this paper develops a subway tunnel safety and rapid assessment method based on slice-based fitting technique. It can use the shortest time to determine the area which has the overrun deformation, and the assessment of the subway tunnel safety can be implemented timely.
The research results are applied on the existing subway tunnel deformation monitoring project such as Beijing subway line10, line1, line6and Guangzhou subway line2and Shenzhen subway line1, the validation, feasibility and high performance are fully proved.
论文在总结传统地铁隧道变形监测方法及其问题的基础上,对精密监测基准、智能变形预测、特征点全自动监测、地铁变形区整体监测等方面的理论和方法开展了深入的研究,解决了如何在变形复杂、环境干扰的地铁运营条件中实施准确全面监测的难题。主要研究内容与创新如下：
1.结合穿越地铁工程实践,总结出运营地铁隧道受近接施工扰动变形的主要影响因素,以及监测数据呈现非线性和混沌特性,需要高精度监测等主要特点。在此基础上,重点研究了建立高精度三维监测基准的理论,构建了加权七参数空间三维坐标转换模型,并推导了精密测量定权和平差的数学模型。
2.鉴于地铁监测基准点随着城市地面整体沉降的问题,研究出应用GPS动态监测改正地铁基准点沉降的方法。利用高频GPS坐标时间序列中噪声的自相关性构建了噪声自相关函数,提出基于分形算法的Kalman滤波消噪模型,实现GPS沉降变形的提取与基准点坐标的沉降改正。
3.根据地铁监测基准需要高精度和实时动态的特点,研制了专用的测量标志,使激光跟踪仪、工业全站仪、GPS、地面三维激光扫描仪、测量机器人等多种高精尖仪器测量坐标一体化,实现了精密基准和GPS空间动态基准的统一,从而构建了地铁时空一体化精密监测基准。
4.对当前地铁监测数据量大、变形分析困难、预测准确率低的技术难题,根据地铁受近接施工扰动变形数据的非线性与混沌特性,提出基于PBIL支持向量机的智能变形预测方法。用基于概率分析的PBIL算法对支持向量机的关键参数组合(C,σ,ε)进行设定。并根据变形方向和变形速度体现变形机理的特点,融合了粒子群算法中粒子飞行兼顾个体增益和群体增益的方向和速度的更新机制,精确设定最优参数组合。从而避免人为选择参数的盲目性,极大地提高了支持向量机的预测能力。通过广州地铁穿越工程变形监测数据预测的应用验证了该变形分析方法具有高精度预测能力和实用性。
5.系统总结了用测量机器人对地铁变形特征点实施全自动监测的理论和方法。鉴于监测中存在地铁运营扰动的问题,研究了基于微动改正模型的自动监测技术。研发了测量机器人地铁全自动监测系统,实现了测量机器人监测行为的自动控制、监测数据的管理分析与智能预测。
6.为实现提高辨识危险隐患能力、全面监测地铁变形的目的,提出了利用三维激光扫描技术进行地铁三维全景监测模式及基于NURBS曲面的点云数据变形提取模型,并用工程实验检验了模型的实效性。鉴于地铁监测的实时特点,在监测区域大或高采样率导致点云处理与变形提取的时间开销很大的情况下,研究出一种基于切片拟合技术的地铁隧道变形快速量测方法,可以用最短的时间确定变形超限的区域。
论文的研究成果在北京地铁1号线、10号线、6号线,广州地铁2号线和深圳地铁1号线等既有地铁变形监测工程中得到测试、验证和应用。
During the traversing engineering, to monitor the existing subway tunnel deformation real-time is an important part of ensuring the safety of subway operation.The subway operation characteristics and the environment are considered the relevant theoretical approaches and key technologies about the subway tunnel structure deformation monitoring and deformation analysis are proposed where the subway tunnel is disturbed by the adjacent construction. Monitoring, identified risks and warning are the main research line. Though the key technology research subject the whole process of security control to the existing subway tunnel construction disturbance deformation is fulfilled. As a result we are successful in monitoring in the deformation, identifying risks on the analysis of key technologies, and early warning in the first place, which ensures the safe operation of the subway.
The paper systematically summarizes the operating subway deformation theory and problems of monitoring and makes in-depth research on the benchmarks of precise monitoring, intelligent deformation prediction, automatic monitoring of feature points, subway deformation zone whole monitoring theory and methods. The research kernel and innovation of the paper are as follows.
1. With the combination the traversing subway projects, the paper summed up that the disturbance deformation of operating subway caused by the adjacent construction is influenced by many factors, the monitoring data shows nonlinear and chaotic characteristics and it needs high-precision monitoring. On this basis, the paper focuses on the theory of building three-dimensional high-precision monitoring benchmark, and weighted seven-parameter space three-dimensional coordinate transformation model is constructed, and the mathematical model of precision monitoring the weight and adjustment is derived
2. In view of the status that the subway monitoring reference point will have settlement with the whole urban ground subsidence the paper proposed the method to correct subway reference point settlement with the application of GPS dynamic monitoring. It constructs noise autocorrelation function with the use of noise autocorrelation in the high-frequency GPS coordinate time series, puts forward the Kalman filtering denoising model based on the fractal algorithm, and fulfills GPS subsidence deformation extract and the reference point coordinates settlement corrections.
3. Because the subway benchmarks for monitoring needs high precision and real-time dynamic characteristics, a dedicated survey marks is developed. The coordinates of many sophisticated measure instruments are integrated such as laser tracking device, industrial total station, GPS, terrestrial3D laser scanner, measuring robots and so forth. A precision reference and the unity of the GPS space on a dynamic basis is achieved, and as a result the subway precise spatial and temporal integration benchmarks for monitoring is built.
4. According to technical problems such as the subway monitoring data on the current is huge and the deformation analysis is difficult and the prediction has low accuracy, and according to the subway deformation data disturbed by adjacent construction is nonlinear and chaotic characteristics, the paper proposes the intelligent deformation prediction method based on the PBIL support vector machine (SVM). PBIL algorithm based on probability analysis is used to obtain the key parameters of support vector machine combinations (C,a,s). And in accordance with the characteristics of the deformation mechanism of the deformation direction and deformation rate, the particle swarm optimization (PSO) algorithm is introduced. Its update mechanism of both individual gain and groups'gain of the particle swarm optimization in particle flight direction and speed is used to guide the setting of optimal parameter combinations. Consequently the blindness of human choice of parameters is avoided and support vector machine prediction ability is improved. The deformation monitoring data of Guangzhou subway traversing project verifies that the deformation analysis has high accuracy predictive ability and practicability.
5. The paper summarizes the theories and methods of using automatic monitoring measuring robot to the implementation the subway deformation characteristics points. View of the problem of disturbance in the subway monitoring, the principle of the automatic monitoring of the model based on micro corrections is put forward. Measuring robot subway automatic monitoring system is developed to achieve the automatic control of monitoring behavior, data management and intelligent forecast of monitoring data.
6. To improve the identification of the threats and achieve the capability to fully monitoring subway deformation,3-D panoramic monitoring mode with the use of3D laser scanner is proposed. The panoramic three-dimensional monitoring station layout and scanning, the point cloud data processing principle are researched. The innovative point is the point cloud data deformation extraction model based on the NURBS surface, and engineering experiments are used to test the effectiveness of the model of deformation and extraction. View of the real-time characteristics of subway monitoring, either the large monitoring regional or the higher sampling rate will result in the huge time spending of point cloud processing and deformation extraction, this paper develops a subway tunnel safety and rapid assessment method based on slice-based fitting technique. It can use the shortest time to determine the area which has the overrun deformation, and the assessment of the subway tunnel safety can be implemented timely.
The research results are applied on the existing subway tunnel deformation monitoring project such as Beijing subway line10, line1, line6and Guangzhou subway line2and Shenzhen subway line1, the validation, feasibility and high performance are fully proved.
引文
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