A field experiment on a steel Gerber-truss bridge for damage detection utilizing vehicle-induced vibrations

Abstract

A field experiment was conducted on a real continuous steel Gerber-truss bridge with artificial damage applied. This paper summarizes the results of the experiment for bridge damage detection utilizing traffic-induced vibrations. It investigates the sensitivities of a number of quantities to bridge damage including: the identified modal parameters and their statistical patterns, Nair's damage indicator (NDI) and its statistical pattern, and different sets of measurement points. The modal parameters are identified by autoregressive (AR) time-series models. The decision on bridge health condition is made and the sensitivity of variables is evaluated with the aid of the Mahalanobis-Taguchi system (MTS), a multivariate pattern-recognition tool. Several observations are made as follows. For the modal parameters, although bridge damage detection can be achieved by performing MTS on certain modal parameters of certain sets of measurement points, difficulties were faced in subjective selection of meaningful bridge modes and low sensitivity of the statistical pattern of modal parameters to damage. For NDI, bridge damage detection could be achieved by performing MTS on NDIs of most sets of measurement points. As a damage indicator, NDI was superior to modal parameters. Three main advantages were observed; it doesn't require any subjective decision in calculating NDI thus potential human errors can be prevented and an automatic detection task can be achieved, its statistical pattern has high sensitivity to damage, and finally, it is flexible regarding the choice of sets of measurement points.

Recent progress and future trends on damage identification methods for bridge structures

“…An et al proposed the curvature difference method of strain waveform fractal dimension for damage detection of truss structures using free vibration strain response signals, and both experimental and numerical results indicate that it is effective to detect the damage in boundary conditions and the truss member with high anti‐noise ability; moreover, it can classify the damage types, that is, damage in joints and damage in measured zones of members. Kim et al conducted a field experiment on a real continuous steel Gerber‐truss bridge to detect the damage modeled by artificial damage under the traffic‐induced vibrations; the modal parameters were examined to attempt to indicate the damage, and Nair's damage indicator, which is a time series‐based damage index, is better than the modal parameters to indicate the damage, because its statistical pattern is high sensitive to damage. Yu and Zhu proposed an integrated method combining the damage‐sensitive feature extraction, the higher statistical moments of structural responses, and the fuzzy c‐means clustering algorithm with a time series analysis‐based damage prognosis method for structural damage prognosis of a truss bridge model; damage cases were set up by loosening connection bolts; experimental results based on a six‐bay truss bridge model indicate that the method can detect damage successfully.…”

EMD-Shannon Entropy-Based Methodology to Detect Incipient Damages in a Truss Structure

“…Among different kinds of civil structures, truss-type designs are one of the most employed structures in the design of civil structures because they offer great benefits such as ease of assembly and light weight. In this regard, this type of design has been used to construct bridges, towers, cranes, roof supports, and building skeletons, among others [3][4][5]. Nevertheless, they suffer continuous degradation or failure during their service life because of excitations produced by human or natural hazards, such as earthquakes, tornadoes, hurricanes, and wind, among others [6][7][8].…”

“…In recent years, numerous signal processing strategies (SPS) that analyze vibrations to perform damage detection in civil structures have been proposed, among which the Fast Fourier transform (FFT) and its variants known as frequency response function (FRF) [18][19][20][21][22], multiple signal classification (MUSIC) [3,[23][24][25], Wigner-Ville distribution (WVD) [26,27], and statistical time series models (STSM) [4,[28][29][30][31][32] are the most employed. Nevertheless, although good results have been reported using the aforementioned techniques, they present significant limitations.…”

Damage Identification Algorithm of Hinged Joints for Simply Supported Slab Bridges Based on Modified Hinge Plate Method and Artificial Bee Colony Algorithms

“…Researchers also studied damage identification methods based on other identification indicators. For instance, Blachowski et al [6] proposed a damage localization approach by axial strain accelerations for truss bridges, and Kim et al [7] developed a damage detection method through Nair's [7] damage indicator which was demonstrated superior to the modal parameters. Nevertheless, it cannot effectively analyze the damage of transverse connection components for hinged-slab bridges.…”