An indirect method for bridge mode shapes identification based on wavelet analysis

Abstract

Mode shapes have been playing a vital role in the research and application of bridge structural health monitoring. This paper presents a novel indirect method identifying bridge mode shapes using dynamic responses of a tractortrailer vehicle model, which consists of one tractor and three instrumented trailers. In an effort to eliminate the road roughness effect, accelerations of adjacent trailers are firstly subtracted. Wavelet analysis is then employed to identify bridge mode shapes from the subtracted accelerations in an iterative manner. Furthermore, wavelet denoising algorithm is adopted to improve the identification accuracy in the presence of measurement noise. Systematic numerical simulations, in which a tractor-trailer model passes over an expressway bridge, are conducted in order to investigate the performance of the proposed method. Sensitivity analysis including vehicle speed, class of road roughness, and noise level are studied in this numerical investigation. Results demonstrate that the proposed method is able to identify bridge modal frequencies and mode shapes with satisfactory resolution, accuracy, and robustness.

Drive‐by health monitoring of highway bridges using Bayesian estimation technique for damage classification

“…To reliably capture physical system behavior, a FEM needs to take into consideration influential environmental and operational parameters, for example, temperature effects and road surface roughness. Fortunately, the effects of surface roughness on vehicle–bridge dynamics can be accounted for by either directly modeling the profile within a FEM 22 or using multiple identical vehicles to effectively subtract surface roughness effects from a vehicle's dynamic response 21 . Note that the surface profile employed within a model must match a physical profile such that the model is capable of capturing the true acceleration responses at all time steps corresponding to physical experimental measurements.…”

“…It should be noted that in the study by Khorram et al, 13 the monitoring vehicle was modeled in a simplified manner as a single point load moving at speeds around 1 m/s, and the reliability of the damage index was not tested under varying levels of noise, which has been demonstrated to affect index accuracy 12,14 . A recent publication demonstrates that the aforementioned issues with wavelet‐based damage classification can potentially be overcome by leveraging Morlet wavelets and multiple vehicles to identify bridge mode shapes, which are generally accepted to be more sensitive to damage 21 . The proposed methodology is capable of accurately identifying mode shapes at speeds up to 20 m/s and at noise levels up to 10 % , which seemingly increases the reliability of wavelet‐based damage classification; note, however, that the proposed method has not been tested for damage classification, and thus, it is unknown if this approach will or will not suffer from the same limitations as other wavelet‐based methods.…”

Enhanced drive‐by bridge modal identification via dual Kalman filter and singular spectrum analysis

“…Drive-by bridge health monitoring using responses of passing vehicles has been studied for over two decades. [1][2][3] Unlike the traditional direct methods that require sensor instrumentation on the structure, the drive-by bridge inspection uses instrumented vehicles to acquire and process vibration data of bridges. It facilitates a rapid and cost-effective bridge structural health monitoring approach.…”

A data‐driven approach for regional bridge condition assessment using inspection reports

“…There are two main approaches for building a deterioration model: mechanism‐based and statistics‐based 22 . The former mainly uses chemical and physical deterioration mechanisms concerning the deterioration of the reinforcement and concrete 23,24 . The mechanisms (i.e., ingress of chlorides, bond loss, loss of cross‐section, etc.)…”