Damage Identification in Warren Truss Bridges by Two Different Time–Frequency Algorithms

Abstract

Recently, a number of authors have been focusing on drive-by monitoring methods, exploiting sensors mounted on the vehicle rather than on the bridge to be monitored, with clear advantages in terms of cost and flexibility. This work aims at further exploring the feasibility and effectiveness of novel tools for indirect health monitoring of railway structures, by introducing a higher level of accuracy in damage modelling, achieve more close-to-reality results. A numerical study is carried out by means of a FE 3D model of a short span Warren truss bridge, simulating the dynamic interaction of the bridge/track/train structure. Two kinds of defects are simulated, the first one affecting the connection between the lower chord and the side diagonal member, the second one involving the joint between the cross-girder and the lower chord. Accelerations gathered from the train bogie in different working conditions and for different intensities of the damage level are analyzed through two time-frequency algorithms, namely Continuous Wavelet and Huang-Hilbert transforms, to evaluate their robustness to disturbing factors. Compared to previous studies, a complete 3D model of the rail vehicle, together with a 3D structural scheme of the bridge in place of the 2D equivalent scheme widely adopted in the literature, allow a more detailed and realistic representation of the effects of the bridge damage on the vehicle dynamics. Good numerical results are obtained from both the two algorithms in the case of the time-invariant track profile, whereas the Continuous Wavelet Transform is found to be more robust when a deterioration of track irregularity is simulated.

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

“…The primary idea behind these techniques is that structural damage modifies the mechanical properties of the bridge and, therefore, the dynamic behavior of the vehicle interacting with it [22]. Then, the condition of the bridge may be determined based on the dynamic responses detected on board the train, such as on the axle box, bogie frame, and carbody.…”

“…Then, the condition of the bridge may be determined based on the dynamic responses detected on board the train, such as on the axle box, bogie frame, and carbody. According to Bernardini et al [22], the primary concern regarding the practical applicability of the drive-by method is that the train's response may be affected not only by the condition of the bridge but also by factors unrelated to the damage, such as the level of track irregularities, the vehicle's dynamic response to different passenger loads, and other uncertainties related to the train's speed variation and accurate positioning with respect to the bridge.…”

“…In their paper, Bernardini et al [22] analyzed the performance of two time-frequency algorithms in identifying damages in a short-span Warren truss bridge by using a FE 3D model to simulate the dynamic interaction of the track-train-bridge system. Numerical investigation is conducted, and two types of damage are simulated, the first of which involves the connection between the lower chord and the side diagonal member, and the second of which involves the connection between the cross-girder and the lower chord.…”

Structural Damage Identification Methods in Truss Bridge Structures Using Vibration Analysis: A Review

“…Visual inspection methods are considered less effective. Visual methods provide valuable information when monitoring protocols are in place, but their weakness lies in the dependence on the skills and experience of inspection operators to identify damage [9]. Consequently, the concept of structural health, also known as Structural Health Monitoring (SHM), has been developed in the field of Structural Engineering [10].…”

A MODWT-Based Algorithm for the Identification and Removal of Jumps/Short-Term Distortions in Displacement Measurements Used for Structural Health Monitoring

“…Particularly, EMD-based techniques generate the so-called intrinsic mode functions. When the EMD-based techniques are associated with the Hilbert transform, the resulting combination is known as Hilbert-Huang transform (HHT), which has been used for radar-based vital sign detection, human activity recognition, and dam-age assessment [29][30][31][32]. Although EMD-based algorithms might be utilized for the analysis of non-stationary/nonlinear signals and have demonstrated its robustness and validity for various applications, it still lacks a reliable and generally accepted theoretical foundation, which would allow for convergence proofs and systematic optimization of the method [33].…”