Identification of Physically Simulated Damage on a Footbridge Based on Ambient Vibration Data

Kody, Alyssa; Li, X.; Moaveni, Babak

doi:10.1061/9780784412848.032

Cited by 6 publications

(3 citation statements)

References 13 publications

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“…The bridge is in healthy condition. In order to evaluate the performance of damage detection methods, two different stationary vehicles (a car and a bus) with different masses were placed on the bridge to emulate two different levels of damage severity [21,7]. The three different categories of data were collected in that study are: "Healthy-Data" when the bridge is free of vehicles; "Car-Damage" when a light car vehicle is placed on the bridge close to location A10; and "Bus-Damage" when a heavy bus vehicle is located on the bridge at location A14.…”

Section: The Cable-stayed Bridge Datasetmentioning

confidence: 99%

Online Tensor Decomposition with optimized Stochastic Gradient Descent: an Application in Structural Damage Identification

Anaissi

Suleiman

Zandavi

2020

2020 IEEE Symposium Series on Computational Intelligence (SSCI)

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Structural Health Monitoring (SHM) provides an economic approach which aims to enhance understanding the behavior of structures by continuously collects data through multiple networked sensors attached to the structure. This data is then utilized to gain insight into the health of a structure and make timely and economic decisions about its maintenance. The generated SHM sensing data is non-stationary and exists in a correlated multi-way form which makes the batch/off-line learning and standard two-way matrix analysis unable to capture all of these correlations and relationships. In this sense, the online tensor data analysis has become an essential tool for capturing underlying structures in higher-order datasets stored in a tensor X ∈ R I1×•••×I N . The CANDECOMP/PARAFAC (CP) decomposition has been extensively studied and applied to approximate X by N loading matrices A(1), . . . ,A(N) where N represents the order of the tensor. We propose a novel algorithm, FP-CPD, to parallelize the CANDECOMP/PARAFAC (CP) decomposition of a tensor X ∈ R I1×•••×I N . Our approach is based on stochastic gradient descent (SGD) algorithm which allows us to parallelize the learning process and it is very useful in online setting since it updates X t+1 in one single step. Our SGD algorithm is augmented with Nesterov's Accelerated Gradient (NAG) and perturbation methods to accelerate and guarantee convergence. The

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Section: The Cable-stayed Bridge Datasetmentioning

confidence: 99%

Online Tensor Decomposition with optimized Stochastic Gradient Descent: an Application in Structural Damage Identification

Anaissi

Suleiman

Zandavi

2020

2020 IEEE Symposium Series on Computational Intelligence (SSCI)

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“…In the context of SHM of bridge structures, it is quite a common practice to emulate damage by locating stationary lumped mass on the bridge. [44][45][46][47][48] This additional mass can be treated as a ''damaging event'' for the evaluation purpose since the increment of mass results in similar effect in the bridge dynamic properties as the decrement of stiffness caused by an actual damage. Two extensive field experiments were conducted on this bridge which are referred to ''Bus Damage Test'' and ''Car Damage Test.…”

Section: Emulated Damagementioning

confidence: 99%

A multi-way data analysis approach for structural health monitoring of a cable-stayed bridge

Alamdari

Khoa

Wang

et al. 2018

Structural Health Monitoring

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A large-scale cable-stayed bridge in the state of New South Wales, Australia, has been extensively instrumented with an array of accelerometer, strain gauge, and environmental sensors. The real-time continuous response of the bridge has been collected since July 2016. This study aims at condition assessment of this bridge by investigating three aspects of structural health monitoring including damage detection, damage localization, and damage severity assessment. A novel data analysis algorithm based on incremental multi-way data analysis is proposed to analyze the dynamic response of the bridge. This method applies incremental tensor analysis for data fusion and feature extraction, and further uses one-class support vector machine on this feature to detect anomalies. A total of 15 different damage scenarios were investigated; damage was physically simulated by locating stationary vehicles with different masses at various locations along the span of the bridge to change the condition of the bridge. The effect of damage on the fundamental frequency of the bridge was investigated and a maximum change of 4.4% between the intact and damage states was observed which corresponds to a small severity damage. Our extensive investigations illustrate that the proposed technique can provide reliable characterization of damage in this cable-stayed bridge in terms of detection, localization and assessment. The contribution of the work is threefold; first, an extensive structural health monitoring system was deployed on a cable-stayed bridge in operation; second, an incremental tensor analysis was proposed to analyze time series responses from multiple sensors for online damage identification; and finally, the robustness of the proposed method was validated using extensive field test data by considering various damage scenarios in the presence of environmental variabilities.

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“…The mass was affixed to the inspection plate using silicon sealant tape that was applied to the entire contact surface of the mass. The use of masses to simulate damage has been used on many occasions to assess the capabilities of different SHM/NDE techniques [22,23]. Figure 6b shows the mass was place halfway between the impact location and the center of the plate meaning the mass was approximately 0.215 m from the impact location.…”

Section: Identification Of Pre-existing Artificial Damagementioning

confidence: 99%

Visualization of stress wave propagation via air-coupled acoustic emission sensors

Rivey

Lee

Yang

et al. 2017

Smart Mater. Struct.

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Abstract.We experimentally demonstrate the feasibility of visualizing stress waves propagating in plates using air-coupled acoustic emission sensors. Specifically, we employ a device that embeds arrays of microphones around an optical lens in a helical pattern. By implementing a beamforming technique, this remote sensing system allows us to record wave propagation events in situ via a single-shot and full-field measurement. This is a significant improvement over the conventional wave propagation tracking approaches based on laser doppler vibrometry or digital image correlation techniques. In this paper, we focus on demonstrating the feasibility and efficacy of this air-coupled acoustic emission technique using large metallic plates exposed to external impacts. The visualization results of stress wave propagation will be shown under various impact scenarios. Such wave visualization capability is of tremendous importance from a structural health monitoring and nondestructive evaluation (SHM/NDE) standpoint. The proposed technique can be used to characterize and localize damage by detecting the attenuation, reflection, and scattering of stress waves that occurs at damage locations. This can ultimately lead to the development of new SHM/NDE methods for identifying hidden cracks or delaminations in metallic or composite plate structures simultaneously negating the need for mounted contact sensors.

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Identification of Physically Simulated Damage on a Footbridge Based on Ambient Vibration Data

Cited by 6 publications

References 13 publications

Online Tensor Decomposition with optimized Stochastic Gradient Descent: an Application in Structural Damage Identification

Online Tensor Decomposition with optimized Stochastic Gradient Descent: an Application in Structural Damage Identification

A multi-way data analysis approach for structural health monitoring of a cable-stayed bridge

Visualization of stress wave propagation via air-coupled acoustic emission sensors

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