This paper presents the implementation of a novel monitoring system in which video images and conventional sensor network data are simultaneously analyzed to identify the structural flexibility from the ambient vibrations. The magnitude ratio between the flexibility estimated from known/unknown input force are theoretically derived and decomposed into two parts: ${\alpha }_i^r$ and ${\mathrm{\Theta }}_k$. The first scale factor ${\alpha }_i^r$ related to basic modal parameters can be acquired using the general modal identification methods. Aiming to tackle the difficulty in identifying the second scale factor ${\mathrm{\Theta }}_k$ related to the force intensity, a video stream of traffic is processed to detect and classify vehicles to determine the vehicle's location while displacement measurements are simultaneously collected. By integrating the toll station data, the vehicle loads are assigned to the vehicle on the bridge deck through the uniqueness of the license plate number. Thus, a structural input–output relationship is established to solve the second scale factor ${\mathrm{\Theta }}_k$. Finally, the flexibility $\stackrel{\sim }{flex}$ estimated from the ambient vibration are scaled by $1/{\alpha }_i^r$ and $1/{\mathrm{\Theta }}_k$, respectively to obtain the exact flexibility $fle{x}^2$, which are same as the analytical ones $flex$. Both numerical example and a laboratory test are performed to demonstrate the accuracy of the proposed methodology. The algorithms, approaches, and results given in the paper demonstrate its effectiveness and shows great potential for its application on a real-life bridge's condition assessment.

中文翻译：

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从环境振动中识别桥梁灵活性的计算机视觉辅助方法


本文介绍了一种新型监控系统的实现，其中同时分析视频图像和传统传感器网络数据，以识别环境振动中的结构灵活性。根据已知/未知输入力估计的柔韧性之间的大小比从理论上推导并分解为两部分：${\alpha }_{我}^r\$\backslash alpha\; \_i^r\$$和${\mathrm{\theta }}_k\$\{\{\backslash 西塔\}\_k\}\$$ 。第一个比例因子${\alpha }_{我}^r\$\backslash alpha\; \_i^r\$$与基本模态参数相关的参数可以利用通用的模态辨识方法来获取。旨在解决确定第二比例因子的困难${\mathrm{\theta }}_k\$\{\{\backslash 西塔\}\_k\}\$$与力强度相关，处理交通视频流以检测和分类车辆，以确定车辆的位置，同时收集位移测量值。通过整合收费站数据，通过车牌号的唯一性将车辆荷载分配给桥面上的车辆。由此，建立结构性投入产出关系来求解第二个尺度因子${\mathrm{\theta }}_k\$\{\{\backslash 西塔\}\_k\}\$$ 。 最后，灵活性$\stackrel{～}{f我ex}\$\backslash widetilde\; \{伸缩\}\$$根据环境振动估计的比例为$1/{\alpha }_{我}^r\$1/\backslash alpha\; \_i^r\$$和$1/{\mathrm{\theta }}_k\$1/\{\{\backslash Theta\}\_k\}\$$ ，分别获得精确的灵活性$f我e{x}^2\$fle\{\{x\}^2\}\$$ ，与解析的相同$f我ex\$弹性\$$ 。进行数值示例和实验室测试以证明所提出方法的准确性。本文给出的算法、方法和结果证明了其有效性，并显示出其在现实桥梁状况评估中的巨大应用潜力。