Inverse Filtering for Frequency Identification of Bridges Using Smartphones in Passing Vehicles: Fundamental Developments and Laboratory Verifications

Abstract

This paper puts forward a novel methodology of employing inverse filtering technique to extract bridge features from acceleration signals recorded on passing vehicles using smartphones. Since the vibration of a vehicle moving on a bridge will be affected by various features related to the vehicle, such as suspension and speed, this study focuses on filtering out these effects to extract bridge frequencies. Hence, an inverse filter is designed by employing the spectrum of vibration data of the vehicle when moving off the bridge to form a filter that will remove the car-related frequency content. Later, when the same car is moving on the bridge, this filter is applied to the spectrum of recorded data to suppress the car-related frequencies and amplify the bridge-related frequencies. The effectiveness of the proposed methodology is evaluated with experiments using a custom-built robot car as the vehicle moving over a lab-scale simply supported bridge. Nine combinations of speed and suspension stiffness of the car have been considered to investigate the robustness of the proposed methodology against car features. The results demonstrate that the inverse filtering method offers significant promise for identifying the fundamental frequency of the bridge. Since this approach considers each data source separately and designs a unique filter for each data collection device within each car, it is robust against device and car features.

Indirect health monitoring of bridges using Tikhonov regularization scheme and signal averaging technique

“…Yang et al 31 showed that when one considers the influence of roughness, vehicle responses dominate the acceleration spectrum and overshadows the bridge frequency components. However, recent studies based on Hilbert transform and inverse filtering show promising result in terms of extracting the bridge frequencies 32,33 . Current techniques to deal with the roughness problem are based on subtracting the signal from two adjacent axles which eliminate the effect of road roughness 24,25 .…”

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.…”

Bridge Frequency Scanning Using the Contact-Point Response of an Instrumented 3D Vehicle: Theory and Numerical Simulation

“…It was found that high vehicle damping [20], the increase of vehicle body mass [21], and heavy vehicles [22] could help suppress the vehicle frequency and the efect of road roughness, making the bridge's fundamental frequency highlighted. To eliminate the efects of the vehicle's self-vibration parameters, Shirzad-Ghaleroudkhani and Gül [23] tried to drive the vehicle of the bridge at frst and then on the bridge to inversely flter out the vehicle's frequencies. To overcome the inverse efects of high vehicle speed, wavelet transform, and Hilbert transform were utilized to increase the frequency resolution of vehicle accelerations [24,25].…”