Impact of Road Profile when Detecting a Localised Damage from Bridge Acceleration Response to a Moving Vehicle

Abstract

Abstract. Previous work by the authors have shown that the acceleration response of a damaged beam subject to a constant moving load can be assumed to be made up of three components: 'dynamic', 'static' and 'damage'. Therefore, appropriate filtering of the acceleration signal can be used to highlight the 'damage' component and quantify its severity. This paper builds on these findings to examine if the same approach can be used to identify damage in the more realistic case of a bridge loaded by a sprung vehicle travelling on a road profile. The consideration of a road profile has the effect of exciting the vehicle modes of vibration which will corrupt the spectrum of bridge accelerations with road/vehicle frequencies. Some of these vehicle frequencies may be lower than the first frequency of the bridge and close to the frequency of the 'damage' component. In the latter, the vehicle frequencies are difficult to remove without also filtering part of the 'damage' component out. As a result, the approach is shown to perform best for low vehicle speeds.

Output‐Only Damage Detection of Steel Beam Using Moving Average Filter

“…Accordingly, it was observed that, with increasing the speed of the moving load, the accuracy of the proposed method will be decreased [2,5,21]. Here in this work, the accuracy was studied at three different velocities (0.3, 0.4, and 0.5 m/s), with the results proving the above-mentioned statement (damage detection failed at velocities equal to or greater than 0.5 m/s).…”

“…The wavelet transform output-only method exhibits large potentials for detecting singularities and damage to structures and bridges and can easily identify the damage with a delta value of 0.1 (where delta refers to the ratio of the crack height to the member height) in a noise-free dataset. Hester and González [5] showed that the impact of the road profile can generate a significant error, affecting structural damage results identified by the wavelet-based methods.…”

“…Such singularities in acceleration signals cannot be observed by the naked eye. Several researchers dealt with the acceleration data to find these singularities using different methods, such as waveletbased methods [2][3][4][5], Random Decrement Technique (RDT) [1,[6][7][8][9], and Blind Source Separation (BSS) methods [10][11][12][13][14].…”

Beam Damage Detection Under a Moving Load Using Random Decrement Technique and Savitzky–Golay Filter

“…Therefore, signal processing is necessary to provide a visible form of these signatures. Some of these signal processing-based methods are transformers [3][4][5][6][7][8], time-domain methods (e.g., random decrement technique (RDT)) [1,2,[9][10][11][12][13][14], and source separation methods (e.g., blind source separation (BSS)) [15][16][17][18][19]. To implement a systematic damage detection procedure, a nested loop of RDT and a Savitzky-Golay filter (SGF) is employed in the present paper.…”

“…Hester and Gonzalez [4], Balafas and Kiremidjian [5], and Cantero and Basu [6] employed wavelet transformers to analyze the acceleration data and visualize these singularities. Although they proved that the wavelet transform is a great tool in bridge health monitoring (BHM), the sensitivity of wavelet analysis in noisy environments could not be neglected [7]. In addition to noise sensitivity, the road irregularities, velocity variation, having small velocity, and wheel dimension were other issues that limit the application of wavelet analysis [8].…”