Data Fusion-Based Structural Damage Detection Under Varying Temperature Conditions

Bao, Yuequan; Xia, Yong; Li, Hui; Xu, You Lin; Zhang, Peng

doi:10.1142/s0219455412500526

Cited by 23 publications

(21 citation statements)

References 29 publications

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“…The health monitoring of aging structures and infrastructures [ 1 , 2 , 3 , 4 ] is nowadays becoming more and more important, and can exploit devices and methodologies developed within the field of embedded or inclusive smart technologies [ 5 , 6 , 7 ]. The envisioned structural health monitoring (SHM) systems have to sense in real-time the changing environment, so as to send out early warnings if dangerous situations are approached.…”

Section: Introductionmentioning

confidence: 99%

Damage Detection in Flexible Plates through Reduced-Order Modeling and Hybrid Particle-Kalman Filtering

Capellari

Azam

Mariani

2015

Sensors

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Health monitoring of lightweight structures, like thin flexible plates, is of interest in several engineering fields. In this paper, a recursive Bayesian procedure is proposed to monitor the health of such structures through data collected by a network of optimally placed inertial sensors. As a main drawback of standard monitoring procedures is linked to the computational costs, two remedies are jointly considered: first, an order-reduction of the numerical model used to track the structural dynamics, enforced with proper orthogonal decomposition; and, second, an improved particle filter, which features an extended Kalman updating of each evolving particle before the resampling stage. The former remedy can reduce the number of effective degrees-of-freedom of the structural model to a few only (depending on the excitation), whereas the latter one allows to track the evolution of damage and to locate it thanks to an intricate formulation. To assess the effectiveness of the proposed procedure, the case of a plate subject to bending is investigated; it is shown that, when the procedure is appropriately fed by measurements, damage is efficiently and accurately estimated.

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Section: Introductionmentioning

confidence: 99%

Damage Detection in Flexible Plates through Reduced-Order Modeling and Hybrid Particle-Kalman Filtering

Capellari

Azam

Mariani

2015

Sensors

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“…Bao et al (2013) employed this formulation for data fusion-based structural damage detection under varying temperature conditions. Here, the temperature change effects on the modal parameters were considered in the construction of the likelihood function for θ in equation (13).…”

Section: Applications Of Bayesian Inference In System Identification mentioning

confidence: 99%

State-of-the-art review on Bayesian inference in structural system identification and damage assessment

Huang

Shao

et al. 2018

Advances in Structural Engineering

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136

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Bayesian inference provides a powerful approach to system identification and damage assessment for structures. The application of Bayesian method is motivated by the fact that inverse problems in structural engineering, including structural health monitoring, are typically ill-conditioned and ill-posed when using noisy incomplete data because of various sources of modeling uncertainties. One should not just search for a single ''optimal'' value for the vector of model parameters but rather attempt to describe the whole family of plausible model parameters based on measured data using a Bayesian probabilistic framework. In this article, the fundamental principles of Bayesian analysis and computation are summarized; then a review is given of recent state-of-the-art practices of Bayesian inference in system identification and damage assessment for civil infrastructure. Discussions of the benefits and deficiencies of these approaches, as well as potentially useful avenues for future studies, are also provided. Our focus is on meeting challenges that arise from system identification and damage assessment for the civil infrastructure but our presented theories also have a considerably broader applicability for inverse problems in science and technology.

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“…Moser and Moaveni (2011) developed several nonlinear models to represent the relationship between the identified natural frequencies and measured temperatures. Bao et al (2012) revealed the relationship between the natural frequencies and temperature conditions with the extracted DSFs of a two-story portal frame structure in the laboratory. These methods require the measurement of varying environmental conditions, therefore are not easy to apply to the large-scale structures.…”

Section: Introductionmentioning

confidence: 99%

Structural damage detection by integrating robust PCA and classical PCA for handling environmental variations and imperfect measurement data

Wang

et al. 2022

Advances in Structural Engineering

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This paper proposes a vibration-based structural damage detection approach considering the effects of uncertainties, including environmental variations and random errors that possibly stem from measurement and automatic modal identification. The existing methods that only employ the classical Principle Component Analysis (PCA) have been demonstrated effective to remove the effects of environmental variations while extremely sensitive to random errors. Therefore, the robust PCA is firstly introduced to remove the random errors, especially outliers, that significantly corrupt the low-rank property of the stacked damage sensitive feature (DSF) matrix. Then, the classical PCA is used to extract the environmental variation-free residues, which are inherently damage-dependent and can be used to detect the existence of damage. The problem of missing data is also considered in this study. It is tackled by adding virtual random errors to the locations of missing entities and thus can be addressed by the introduced robust PCA. The advantages of the proposed approach include: (1) Handling the random error-contaminated DSF data regardless of the error’s amplitude, which is an intractable problem for the existing classical PCA-based methods to consider the environmental effects; (2) Damage detection process can be automatic since the missing data can be automatically predicted and the random errors are not required to be manually distinguished. The effectiveness and performance of the proposed method are demonstrated on a numerical beam structure and an experimentally tested wooden bridge model.

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Data Fusion-Based Structural Damage Detection Under Varying Temperature Conditions

Cited by 23 publications

References 29 publications

Damage Detection in Flexible Plates through Reduced-Order Modeling and Hybrid Particle-Kalman Filtering

Damage Detection in Flexible Plates through Reduced-Order Modeling and Hybrid Particle-Kalman Filtering

State-of-the-art review on Bayesian inference in structural system identification and damage assessment

Structural damage detection by integrating robust PCA and classical PCA for handling environmental variations and imperfect measurement data

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