Bayesian model updating of a full-scale finite element model with sensitivity-based clustering

Jang, Jinwoo; Smyth, Andrew W.

doi:10.1002/stc.2004

Cited by 52 publications

(50 citation statements)

References 53 publications

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“…For mode V2 and V3, the frequencies identified from the displacements matched very well with the reference data. The frequency of mode T1 (0.40 Hz) identified from the EEM displacement was the same to that identified from the acceleration data collected in 2016, but was 8.1% larger than the reference data (0.37 Hz) recorded in earlier years in literature before 2009 (Mayer et al., ; Jang and Smyth, ). The increase in the torsional mode frequency T1 as identified from the EEM displacements obtained in 2017 was due to an increase of torsional stiffness by installing truss stiffening to reduce twisting in the Manhattan Bridge Reconstruction Program, which was completed around 2010.…”

Section: Field Tests To Validate the Edge‐enhanced Matching Techniquesupporting

confidence: 53%

“…The video camera was located near Jane's Carousel at 330 m away from the mid‐span of Manhattan Bridge to capture its vibrational responses as shown in Figure . Based on a prior study, the first three major vertical mode frequencies of the Manhattan Bridge are below 0.5 Hz (Jang and Smyth, ). The displacement sampling rate in this field experiment was set to 60 Hz, which is sufficient for this study as well as future analyses of higher order modes.…”

Section: Field Tests To Validate the Edge‐enhanced Matching Techniquementioning

confidence: 99%

“…The identified mode frequencies are then compared with the reference data listed in Table . Based on the data in literature (Jang and Smyth, ), the four identified mode frequencies are the mode frequencies of the four bridge vibrational modes including: the first vertical mode (V1), the second vertical mode (V2), the first torsional mode (T1), and the third vertical mode (V3). The torsional mode T1 could be detected as well as the vertical modes as the torsional vibration also resulted in vertical displacements.…”

Section: Field Tests To Validate the Edge‐enhanced Matching Techniquementioning

confidence: 99%

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Edge‐Enhanced Matching for Gradient‐Based Computer Vision Displacement Measurement

Luo

Feng

2018

Computer aided Civil Eng

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Computer vision‐based displacement measurement for structural monitoring has grown popular. However, tracking natural low‐contrast targets in low‐illumination conditions is inevitable for vision sensors in the field measurement, which poses challenges for intensity‐based vision‐sensing techniques. A new edge‐enhanced‐matching (EEM) technique improved from the previous orientation‐code‐matching (OCM) technique is proposed to enable robust tracking of low‐contrast features. Besides extracting gradient orientations from images as OCM, the proposed EEM technique also utilizes gradient magnitudes to identify and enhance subtle edge features to form EEM images. A ranked‐segmentation filtering technique is also developed to post‐process EEM images to make it easier to identify edge features. The robustness and accuracy of EEM in tracking low‐contrast features are validated in comparison with OCM in the field tests conducted on a railroad bridge and the long‐span Manhattan Bridge. Frequency domain analyses are also performed to further validate the displacement accuracy.

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Section: Field Tests To Validate the Edge‐enhanced Matching Techniquesupporting

confidence: 53%

Section: Field Tests To Validate the Edge‐enhanced Matching Techniquementioning

confidence: 99%

Section: Field Tests To Validate the Edge‐enhanced Matching Techniquementioning

confidence: 99%

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Edge‐Enhanced Matching for Gradient‐Based Computer Vision Displacement Measurement

Luo

Feng

2018

Computer aided Civil Eng

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“…For this purpose, it is more expedient to minimize the negative logarithm likelihood function given by

\begin{matrix} true \\ δ P^{italicK} = arg min 〈J (δ P^{italicK})〉; \\ J (δ P^{italicK}) = \frac{1}{2} \sum_{i = 1}^{N_{0}} {((), E_{d} - E ((), δ P^{K}))}_{i}^{T} {normalΣ}_{i}^{- 1} (E_{d} - E {((), δ P^{K})}_{i}) + \frac{1}{2} \sum_{i = 1}^{N_{m}} {S_{i}}^{- 2} {((), ω_{d} - normalω ((), δ P^{K}))}_{i}^{2} \end{matrix} .

One of the major challenges in a Bayesian damage detection or model updating procedure is the application of the method on a large‐scale structure with a large number of unknown parameters. Some researchers studied the Bayesian model updating of a large scale model . Generally, for damage identification in structures with a large number of DOFs, it is necessary to utilize some methods to alleviate computational problems, including substructure methods, model reduction, coarse meshing, and parameter grouping techniques Yuen detected probabilistic damage in a hundred‐story building using a substructuring approach, which allowed for identification of only critical substructures …”

Section: Proposed Methodsmentioning

confidence: 99%

“…In cases with a large number of parameters to be optimized, using a more computationally efficient procedure for converging to the joint probability distribution of Equation is beneficial. Jang and Smyth utilized the Hamiltonian Monte Carlo method for dealing with the high‐dimensional parameter space and highly correlated parameters. The Hamiltonian Monte Carlo is most robust in dealing with high‐dimensional problems.…”

Section: Proposed Methodsmentioning

confidence: 99%

Transfer function‐based Bayesian damage detection under seismic excitation

Vahedi

Khoshnoudian

Hsu

et al. 2019

Structural Design Tall Build

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Summary Transfer function (TF) data are recognized as diagnostic features in damage detection procedure. The objective of this paper is to present a damage detection method in Bayesian paradigm based on TF data due to ground excitation. The measured seismic responses of the structure in the frequency domain are adopted to obtain displacement TFs and the structural natural frequencies are identified from observed TFs. The derived features are utilized for Bayesian structural damage detection. In addition, the challenging issue of underlying flexible soil in real cases has been addressed. The proposed technique is applied to a numerical shear frame to evaluate the capability of the method. An experimental study on a six‐story steel building has been validated to demonstrate the capability of the method for damage detection purpose. The results of studied cases indicated that the proposed method is capable of identifying the location and the severity of damage precisely.

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Data‐driven models for temperature distribution effects on natural frequencies and thermal prestress modeling

Jang

Smyth

2019

Struct Control Health Monit

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In vibration-based damage detection, it is fundamentally important to distinguish between changes in the dynamics of a structure and those related to environmental effects. Modal properties of structures are widely used as damage-sensitive features and indicate changes in physical properties due to structural damage or degradation. However, civil structures such as bridges are influenced by various operational and environmental conditions (such as traffic, wind, humidity, and, most importantly, temperature), and these variables also alter the modal properties of structures. Changes in modal properties caused by environmental effects can mask the effect of structural damage, resulting in unreliable structural damage assessments. It is thus fundamentally important to understand the relationship between environmental effects and modal properties. In this paper, a full-scale finite element (FE) model is used in a simulation-based study to investigate the effects of temperature distribution on natural frequencies. Two approaches for modeling the temperature effects on natural frequencies (thermal prestress imposed at an element level and temperature-dependent Young's modulus) are implemented in the FE model, and results are compared. Then, temperature profiles of the model are hypothetically simulated. These simulation results are used to develop a linear model and the three most popular machine learning models to reproduce the effects of temperature distribution on natural frequencies. Finally, a comprehensive comparison is made between the performances of these data-driven models. KEYWORDS machine learning, regression model, system identification, temperature effects on natural frequencies, thermal prestress Struct Control Health Monit. 2020;27:e2489.wileyonlinelibrary.com/journal/stc

show abstract

Bayesian model updating of a full-scale finite element model with sensitivity-based clustering

Cited by 52 publications

References 53 publications

Edge‐Enhanced Matching for Gradient‐Based Computer Vision Displacement Measurement

Edge‐Enhanced Matching for Gradient‐Based Computer Vision Displacement Measurement

Transfer function‐based Bayesian damage detection under seismic excitation

Data‐driven models for temperature distribution effects on natural frequencies and thermal prestress modeling

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