大型结构动态位移全天候摄像测量环境影响及数据融合研究

Displacement is a good descriptor of the structural behavior and safety status. It's also a key monitoring item in structural construction monitoring and structural health monitoring. However, measuring displacement of structures under dynamic excitations is still a challenging task. Videogrammetry shows great potential for dynamic displacement measurement, benefiting from its non-contact and long-distance characteristics. Nevertheless, its all-weather performance has to be fully evaluated and improved when necessary before gaining wide applications. This study carries out an investigation into the environmental effects and data fusion techniques of the all-weather videogrammetry for structural dynamic displacement monitoring. First, long-term outdoor dynamic displacement monitoring tests will be carried out. In the test, virtual structural displacement is generated by a motion simulation device and monitored by a commercialized industrial digital camera. Meanwhile, the surrounding environment will be monitored. Making use of the long-term displacement and environment monitoring data, the correlation model between environment and displacement error will then be established with the artificial neural network. From this model, the environmental effects will be disclosed and the most adverse environment will be identified. Based on this, the approach to evaluate the all-weather performance will be formulated. After that, two techniques will be established to improve the displacement accuracy, especially under the most adverse environment. The first technique uses displacement data only. It employs the adaptive filter to filter out noises, which has the primary input of the displacement in major direction and the reference input of the displacement in minor direction. The second technique needs collocated displacement and acceleration data. The displacement is fused with the collocated acceleration using the Kalman filter to improve its accuracy as well as frequency bandwidth. Finally, field tests on the Jiaojiang 2nd Bridge in Taizhou, Zhejiang will be carried out to validate the all-weather performance of the videogrammetry for structural dynamic displacement monitoring.

位移是反映结构性能和安全的重要指标。大型结构动态位移监测仍存在较多困难。结构位移摄像测量具有非接触、远距离等优点，有望成为一种实用方法，但其全天候工作性能评估及优化有待研究。鉴于此，本项目开展大型结构动态位移全天候摄像测量环境影响及数据融合研究，环境影响研究服务全天候工作性能评估，数据融合研究服务全天候工作性能优化。首先，利用位移模拟装置和工业级数字摄像机，开展大型结构动态位移全天候摄像测量长期室外试验，为全天候工作性能评估提供数据支持。然后，利用长期的环境和位移监测数据，采用人工神经网络方法揭示环境影响规律及最不利环境因素，据此建立全天候工作性能评估依据和方法。接着，针对不利环境，采用数据融合技术优化位移精度，据此形成全天候工作性能优化方法。最后，开展大型结构动态位移全天候摄像测量现场试验，验证全天候工作性能。本研究可为大型结构动态位移全天候摄像测量的发展和应用提供理论依据和技术支持。

位移是反映结构性能和安全的重要指标。大型结构动态位移监测仍存在较多困难。摄像测量具有非接触、远距离等优点，有望成为一种实用方法，但其全天候工作性能有待精细化评估及优化。鉴于此，本项目开展了大型结构动态位移全天候摄像测量环境影响及数据融合研究。首先，进行了大型结构动态位移全天候摄像测量长期室外试验。在温州大学校园内建立了室外、自动、连续、长期的结构动态位移摄像测量系统，自主设计了一套位移模拟装置来产生虚拟结构动态位移，由工业级数字摄像机实时监测。将试验场地分为楼顶和地面两类，将仪器支撑分为有无三角架两类，组合而成四类试验，从而更为详尽地分析建筑物位移、仪器支撑等因素对摄像测量精度的影响。然后，进行了大型结构动态位移全天候摄像测量环境影响研究。利用长期的环境和位移监测数据，识别得到了摄像测量的最不利环境因素，它们分别为温度、降雨和强风，并揭示了它们对摄像测量的环境影响规律。气温对摄像测量的影响呈现明显的周期性，温度与位移测量误差之间存在显著的相关性。降雨对摄像测量产生不可忽视的影响，降雨引起的水平位移测量误差和竖向位移测量误差存在显著的相关性，且误差以低频成分为主。强风对摄像测量产生十分显著的影响，强风引起的位移测量误差以高频成分为主。接着，针对上述最不利环境因素，进行了摄像测量系统误差及随机噪声抑制方法研究，形成了摄像测量全天候工作性能优化方法。利用大型结构在某一方向的动态位移极小的特点，形成了自适应滤波降噪技术。由于降雨引起的水平位移测量误差和竖向位移测量误差存在显著的相关性，该方法能比较有效地降低降雨引起位移测量误差。利用实测位移的真实信号与噪声的能量分布不同的特点，形成了小波降噪技术。相对结构动态位移而言，降雨引起的位移测量误差以低频成分为主，强风引起的位移测量误差则以高频成分为主，该方法能十分有效地降低降雨或强风引起的位移测量误差。最后，开展了大型结构动态全天候位移摄像测量现场试验，验证全天候工作性能。本项目研究结果为大型结构动态位移全天候摄像测量的发展和应用提供了数据支持和理论依据，为实现大型结构动态位移全天候摄像测量奠定了基础，具有较好的科学意义和工程应用价值。