基于正交异性桥面板应力监测数据的车辆荷载识别
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U441+.2

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基于准分布长标距FBG和深度信念网络的组合梁损伤识别研究


Vehicle load identification based on stress monitoring data of orthotropic bridge deck
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Damage identification of composite beams based on quasi distributed Long scale FBG and deep belief network

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    摘要:

    研究了基于正交异性桥面板应力监测数据的车辆荷载识别技术。通过在正交异性桥面板顶板U肋下缘布设应变计,采集车辆通过时的应力响应。首先利用同一U肋不同截面测点应力响应间的互相关函数来估计车速,针对车辆作用下同一截面下不同测点的应力响应面积的变化,提出了余弦相似度这一指标实现车辆横向定位,最后通过比较实测应力响应面积与标定荷载作用下响应面积来实现未知车辆荷载的识别。通过数值模拟验证了算法的有效性和抗噪性。最后设计了一个模型试验进一步验证了提出的理论。数值和试验结果表明,提出的算法可以有效识别车辆荷载,算法具有较好的抗噪性能,提出的标定间距可以为工程结构提供具有实操性的指导。

    Abstract:

    Vehicle load identification technology based on stress monitoring data of orthotropic bridge decks is studied. Some strain gauges were arranged at the lower edge of the U-rib to measure the stress response when the vehicles cross the deck. Firstly, an index based on cross-correlation function of stress response between different measurement points on the same U-rib is used to evaluate vehicle speed. Considering the stress response area variation of different measurement points on the same section, a Cosine similarity index is proposed to location the transverse position of vehicle. Finally, the unknown vehicle load can be identified by comparing the measured stress response area with the calibration result. The effectiveness and anti-noise performance of the proposed method are verified by numerical simulation. In order to further verify the performance of the algorithm, a model test is designed and carried out. Numerical and experimental results show that the proposed method can effectively identify the vehicle load with good anti-noise performance. And a calibration space is provided for guiding practical engineering application.

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王超,周卓升,贺伟诚,等. 基于正交异性桥面板应力监测数据的车辆荷载识别[J]. 科学技术与工程, 2022, 22(16): 6695-6701.
Wang Chao, Zhou Zhuosheng, He Weicheng, et al. Vehicle load identification based on stress monitoring data of orthotropic bridge deck[J]. Science Technology and Engineering,2022,22(16):6695-6701.

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  • 收稿日期:2021-08-27
  • 最后修改日期:2022-03-04
  • 录用日期:2021-12-26
  • 在线发布日期: 2022-06-22
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