李程. 2017: 基于加速度传感器数据融合的隧道施工地面沉降监测技术研究. 工程地质学报, 25(s1): 60-67. DOI: 10.13544/j.cnki.jeg.2017.s1.010
    引用本文: 李程. 2017: 基于加速度传感器数据融合的隧道施工地面沉降监测技术研究. 工程地质学报, 25(s1): 60-67. DOI: 10.13544/j.cnki.jeg.2017.s1.010
    LI Cheng. 2017: STUDY OF ACCELEROMETER DATA FUSION IN MONITORING OF GROUND SUBSIDENCE INDUCED BY TUNNEL CONSTRUCTION. JOURNAL OF ENGINEERING GEOLOGY, 25(s1): 60-67. DOI: 10.13544/j.cnki.jeg.2017.s1.010
    Citation: LI Cheng. 2017: STUDY OF ACCELEROMETER DATA FUSION IN MONITORING OF GROUND SUBSIDENCE INDUCED BY TUNNEL CONSTRUCTION. JOURNAL OF ENGINEERING GEOLOGY, 25(s1): 60-67. DOI: 10.13544/j.cnki.jeg.2017.s1.010

    基于加速度传感器数据融合的隧道施工地面沉降监测技术研究

    STUDY OF ACCELEROMETER DATA FUSION IN MONITORING OF GROUND SUBSIDENCE INDUCED BY TUNNEL CONSTRUCTION

    • 摘要: 地面沉降的监测和控制一直是制约隧道施工安全的关键问题,目前地表沉降量一般利用水准仪等传统设备、通过人工定期测量获取,无法进行实时动态观测。随着无线传感器网络和微机电系统的高速发展,通过在地表布置无线传感器节点,即可对隧道施工引起的地面沉降进行实时监测。本文对处于盾构施工期的某隧道开展了基于加速度传感器数据融合的地面沉降监测技术研究。首先,在隧道上方和两侧布置了内嵌两套加速度传感器的节点盒,获取原始监测数据;然后,利用旋转矩阵求解其空间法向量,并通过投影得到地表节点在隧道轴向上的倾斜角;最终,通过集中式卡尔曼滤波对上述两类传感器的倾斜角进行融合估计,推导出传感器倾斜角随时间的变化情况。基于该方法,通过布置传感器节点监测网,即可估算隧道施工引起的地表绝对沉降量。

       

      Abstract: Monitoring and controlling of tunneling caused ground subsidence is a key issue related to construction safety. Instead of real-time monitoring, manually measurement using total station or leveling instrument is usually conducted at regular intervals. With the rapid development of wireless sensor networks and microelectro mechanical systems, geological engineers are able to carry out a real-time wireless monitoring of ground subsidence through sensor nodes deployed on the ground. In this paper, we provide a study of accelerometer data fusion in the use of ground settlement monitoring based on surface measurements during shield tunneling. Initially, two sets of sensor nodes were placed at the surface above the excavated tunnel; after obtaining the raw data, the normal vectors of the sensors were firstly derived with the help of rotation matrices, then the dip angles over time were obtained from the projection of normal vectors in the longitudinal section; finally, a centralized Kalman filter was applied to estimate the tilt angles of the sensor nodes based on the data from two respective sets of accelerometers. Using this method, the absolute ground settlement can be achieved via a distributed deployment of sensor nodes.

       

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