An algorithm for damage localization in steel truss structures: Numerical simulation and experimental validation

Abstract

It is imperative to study the damage detection methods of steel truss structures that are always employed in extreme environment. Accurate structural damage localization is still a challenge due to high noise and low accuracy of the structural finite element model. To develop a dependable damage localization technique for truss structural health monitoring, a novel idea of damage localization is proposed: the curvature difference method of strain waveform fractal dimension, based on fractal theory and curvature method. To validate the approach, a simply supported bailey steel truss benchmark model has been designed and constructed in the laboratory. Based on the model, both experimental and numerical simulation results using the procedure under pulse excitation indicate that it is feasible and effective to detect the change of boundary conditions and the stiffness reduction of a truss member. In addition, the proposed technique exhibits high-noise insusceptibility (e.g. it works for noise levels up to 20% for a 10% truss member stiffness reduction). Moreover, the proposed technology is robust against the accuracy of the finite element model of measured structures, which decrease the workload of model updating dramatically. All these lay a good foundation for its engineering application.

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

“…Despite extensive research on structural damage identification in the last several decades, most of them rely on numerical simulation and laboratory experiments, while full‐scale validation remains limited. Three major challenges are met in the vibration‐based damage identification for bridge structures: First, accurate structural damage identification, and damage severity identification in particular, is challenging due to three main difficulties: high noise, low sensitivity to small damage, and low accuracy of the numerical model . Second, studies on damage identification for beam bridges and truss bridges have been extensively published; however, little has been published concerning arch bridges, cable‐stayed bridges, and suspension bridges, and even fewer works deal with experiments and engineering application.…”

“…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.…”

Structural damage detection based on iteratively reweighted l1 regularization algorithm

“…The destruction of most steel structures is caused by the failure of joints. Therefore, the safety of joints is essential to steel structures (An et al, 2013). In this regard, damage in bars and joints of the frame model are investigated in this study.…”

Damage identification in a complex truss structure using modal characteristics correlation method and sensitivity-weighted search space

“…[14][15][16][17] Some researchers have attempted to examine more complex truss structures, but they only considered a small region of the structure to test their methodologies. 18,19 Therefore, it will be beneficial to treat a truss with a large number of members in the damage identification study.…”