Bridge modal property identification based on asynchronous mobile sensing data

Abstract

There is a growing attention in real-time bridge condition assessment using data from drive-by vehicles as a potentially scalable approach. Most system identification methods are based on synchronized vibration data collection for this purpose. This study presents an approach for bridge modal identification that estimates high-resolution absolute value of the operational mode shapes using asynchronous mobile data. With each trip of a vehicular sensor, the spatio-temporal response of the bridge is sampled, along with various sources of noise, e.g. vehicle dynamics, environmental effects, road profile, etc. The crowdsourced modal identification using continuous wavelet (CMICW) method is proposed that gradually magnifies the bridge dynamical signatures and mitigates noise over the spatio-temporal map. The performance of the CMICW method is validated in an experimental setting. The method successfully identifies natural frequencies and absolute value of the operational mode shapes of a bridge with high resolution and accuracy. Notably, by including data collected from various bridge lanes, the method can reconstruct 3D representation of the mode shapes. The influence of the speed of the mobile sensors on the accuracy of the estimated modal properties is investigated as well. Using a hybrid simulation framework, the effect of vehicle dynamic is included in the mobile sensing data. The study shows that the CMICW method is successful in discounting the effect of vehicle dynamics, thereby strengthening the bridge modal information. Finally, a blind source separation technique is implemented to separate the effects of road irregularities, which further improves the accuracy of modal property estimates. This study contributes to the growing body of knowledge on mobile crowdsensing for physical properties of transportation infrastructure.

Smartphone Prospects in Bridge Structural Health Monitoring, a Literature Review

“…More research followed the pioneers above; numerous researchers performed proof-of-concept tests to retrieve modal frequency data from moving vehicles instrumented with smartphones [ 122 , 123 , 124 ]. Likewise, more advanced techniques were developed, such as an inverse filtering approach for frequency identification [ 125 ] and more complex drive-by modal analyses [ 126 ] and clock-asynchronous data [ 127 ]. Concerning bridges, the drive-by data encapsulates the mechanical features of the bridge, as well as the vehicle, and the interaction with each other.…”

Drive-by Methodologies Applied to Railway Infrastructure Subsystems: A Literature Review—Part I: Bridges and Viaducts

“…A scaled laboratory test was conducted, and the bridge's first two mode shapes were successfully identified for a low level of irregularities. In another study involving scaled experimental validation, Eshkevari et al [51] proposed the Crowdsourced Modal Identification using the Continuous Wavelet (CMICW) technique So far, most of the studies involving drive-by methodologies for mode shape extraction have been based only on the SDOF models without any experimental validation. Seeking to further advance in this topic, Zhou et al [50] proposed a methodology for bridge mode shape extraction based on the vertical acceleration of a two-axle vehicle crossing a bridge.…”

“…A scaled laboratory test was conducted, and the bridge's first two mode shapes were successfully identified for a low level of irregularities. In another study involving scaled experimental validation, Eshkevari et al [51] proposed the Crowdsourced Modal Identification using the Continuous Wavelet (CMICW) technique for the extraction of the mode shapes of a bridge. This technique relies on the use of the Continuous Wavelet Transform (CTW) method averaged for a large number of vehicles (crowd) to extract the absolute value of the mode shapes, as it is presented in the flowchart of Figure 10a.…”

Spatiotemporal extrapolation for vibration to strain estimation using domain adaptation

“…The methods that employ static response measurements such as displacement, curvature, and strain are known to be expensive due to the high labor and equipment cost, although they turn out very effective in SHM, especially in the damage localization [1]. Furthermore, considerable advances have been obtained due to the utilization of mobile sensors which allows the collection of extensive spatial vibration of the bridge [2,3].…”