反转结构基底剪力的非接触式测量方法

IF 4.6 2区 工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY
Wei Guo, Yan Long, Yikai Luo, Ruyi Jin, Longlong Guo
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引用次数: 0

摘要

针对大型结构中测量基底剪力所面临的复杂安装挑战和传感器的高昂成本,本文提出了一种集成计算机视觉和模型更新的非接触式测量方法,用于反演结构基底剪力。计算机视觉部分测量物理位移,而非线性模型更新部分则通过完善结构数值模型来反演基底剪力,从而实现经济高效的非接触式反演测量。在计算机视觉部分,选择了一种高实时性和高精度的光流估计算法,并在推杆运动跟踪测试中进行了验证,其位移跟踪和传感器测量结果之间的归一化均方根误差小于 3%。模型更新部分采用了 Bouc-Wen 模型,通过数值模拟证明了该模型能够在各种噪声干扰水平下在 7000 步内快速校准数值模型,准确获取结构基底剪力。此外,还讨论了不同响应组合和采样频率对模型更新参数识别的影响。研究结果表明,当同时考虑位移和加速度以及 200 Hz 的采样频率时,由于降低了对测量噪声的敏感性,参数识别可以满足精度要求。此外,还对三层剪力框架进行了振动台试验,以进一步验证所提方法的可行性。试验结果表明,振动台试验识别结果的振幅和波动趋势在前 25 秒内与数值模拟结果一致,峰值误差为 18.9%。虽然误差相对较大,但本文为模型更新和结构健康监测提供了一个实用的研究框架。同时,它还降低了在测试过程中获取结构响应数据的成本,从而促进了计算机视觉技术在结构响应监测领域的应用和推广。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Noncontact Measurement Method for Inverting Structural Base Shear

Noncontact Measurement Method for Inverting Structural Base Shear

In response to the intricate installation challenges and the elevated cost of sensors for measuring base shear in large-scale structures, this paper proposes a noncontact measurement method integrating computer vision and model updating to invert structural base shear. The computer vision part measures physical displacement, while the nonlinear model updating section inverts base shear by refining the structural numerical model, thus achieving cost-effective, noncontact inverting measurements. In the computer vision component, a highly real-time and accurate optical flow estimation algorithm was selected and validated in actuator motion tracking tests, yielding a normalized root mean square error of less than 3% between displacement tracking and sensor measurable results. The model-updating section adopts the Bouc–Wen model, demonstrating through numerical simulations its ability to swiftly calibrate the numerical model within 7000 steps under various noise interference levels, accurately obtaining structural base shear. Moreover, the influence of different response combinations and sampling frequencies on parameter identification for model updating is discussed. Findings indicate that when considering both displacement and acceleration, along with a sampling frequency of 200 Hz, parameter identification meets accuracy requirements due to reduced susceptibility to measurement noise. In addition, a shake table test on a three-layer shear frame is conducted to further validate the proposed method’s feasibility. Test results demonstrate that the amplitude and fluctuation trend of the shake table test’s identification results mirror those of the numerical simulation results within the first 25 seconds, with a peak value error of 18.9%. While the error is relatively large, this paper provides a practical research framework for model updating and structural health monitoring. Simultaneously, it reduces the cost of acquiring structural response data during tests, thereby facilitating the application and promotion of computer vision technology in structural response monitoring.

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来源期刊
Structural Control & Health Monitoring
Structural Control & Health Monitoring 工程技术-工程:土木
CiteScore
9.50
自引率
13.00%
发文量
234
审稿时长
8 months
期刊介绍: The Journal Structural Control and Health Monitoring encompasses all theoretical and technological aspects of structural control, structural health monitoring theory and smart materials and structures. The journal focuses on aerospace, civil, infrastructure and mechanical engineering applications. Original contributions based on analytical, computational and experimental methods are solicited in three main areas: monitoring, control, and smart materials and structures, covering subjects such as system identification, health monitoring, health diagnostics, multi-functional materials, signal processing, sensor technology, passive, active and semi active control schemes and implementations, shape memory alloys, piezoelectrics and mechatronics. Also of interest are actuator design, dynamic systems, dynamic stability, artificial intelligence tools, data acquisition, wireless communications, measurements, MEMS/NEMS sensors for local damage detection, optical fibre sensors for health monitoring, remote control of monitoring systems, sensor-logger combinations for mobile applications, corrosion sensors, scour indicators and experimental techniques.
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