Rapid Impact Testing and System Identification of Footbridges Using Particle Image Velocimetry

Abstract

The rapid impact testing of bridges contains unique advantages. For example, structural parameters, including frequency response function and structural flexibility matrix can be identified; however, additional impact-testing instruments are required to excite a bridge, restricting the efficiency of the measurement strategy in terms of experimental cost and time. In this paper, a particle image velocimetry-based method is proposed for the rapid impact testing and system identification of footbridges under pedestrian excitations. The proposed method has shown promising features: (1) pedestrian load is utilized for the impact excitation of footbridges, which is more convenient than the conventional impacttesting method with additional excitation devices; (2) the human-induced impact forces under varying jumping scenarios are calculated from image sequences of human motions acquired by a single camera with its noncontact and target-less characteristics; and (3) both humaninduced impact forces (inputs) and structural responses (outputs) are employed to identify more modal parameters (i.e., scaling factors, modal mass, and structural flexibility). The robustness of the proposed method was successfully validated by a laboratory test of a simply supported beam and field testing of a cable-stayed footbridge. The proposed method not only could improve

Noncontact cable force estimation with unmanned aerial vehicle and computer vision

“…In civil engineering, the fixed camera was widely adopted for acquiring image sequences of the cable from a long distance and the commonly used displacement calculation method was the correlation‐based matching algorithm. The PIV (Tian et al., 2019b) was adopted in this article for extracting cable displacements and results were compared to those calculated from proposed line segments detection and matching algorithm. The principle of the traditional correlation‐based method is to select the ROI from captured video and define the subset size for calculation.…”

“…Park, Lee, Adeli, and Lee (2007) presented a displacement measurement model for health monitoring of structure using terrestrial laser scanning. Then, various system identification methods were developed for cable dynamic properties identification, such as frequency domain decomposition, Complex Mode Indicator Function (CMIF) (Tian, Zhang, & Yu, 2019a), stochastic subspace identification (Tian, Zhang, & Yu, 2019b), synchrosqueezed wavelet transform (Amezquita‐Sanchez & Adeli, 2015; Perez‐Ramirez et al., 2016), MUSIC (Amezquita‐Sanchez, Park, & Adeli, 2017), and Hilbert Transforms (Z. Li, Park, & Adeli, 2017). In recent years, vision‐based system has become a powerful approach for displacement/vibration measurement in civil engineering (Dong, Celik, & Catbas, 2018; Dong, Celik, Catbas, O'Brien, & Taylor, 2020; Luo & Feng, 2018; S.W.…”

“…In recent years, vision‐based system has become a powerful approach for displacement/vibration measurement in civil engineering (Dong, Celik, & Catbas, 2018; Dong, Celik, Catbas, O'Brien, & Taylor, 2020; Luo & Feng, 2018; S.W. Park, Park, Kim, & Adeli, 2015; Tian et al., 2019a), human‐induced impacting force measurement (Celik, Dong, & Catbas, 2018; Tian et al., 2019b), and cable force estimation (Feng, Scarangello, Feng, & Ye, 2017; Kim, Jeon, Kim, & Park, 2013; Yan, Chen, Yu, & Jiang, 2019). Although vision‐based system has been applied for cable dynamic properties estimation, it needs to be installed on a stationary tripod and sometimes the camera needs to be moved from one location to another location for the measurement of all cables owing to the limited field of view (FOV) of installed camera.…”

Time‐varying frequency‐based scaled flexibility identification of a posttensioned concrete bridge through vehicle–bridge interaction analysis

“…To eliminate the effect of measurement error and parameters identification error on the scaling factor, the scaling factor calculated by Equation 7 should be averaged after the outlier value is eliminated. After structural scaling factor is obtained, mass‐normalized modal shapes and scaled flexibility matrix can be further calculated 28 . It should be mentioned that the proposed method for scaled flexibility identification requires identifying time‐varying frequencies of the VBI system from moving truck‐induced dynamic responses.…”

A review of computer vision–based structural health monitoring at local and global levels

“…Hosseini et al 170 monitored the full field displacement and strain of steel and RC beams using PIV in laboratory. Tian et al 171 implemented PIV to monitor the displacement of the human jumping in vertical direction. Although it is stated that the reason of the selection of PIV was because PIV could achieve more accurate measurement than vision-based methods, no comparative studies were provided to verify the statement in that study.…”