Modal Identification Study of Vincent Thomas Bridge Using Simulated Wind‐Induced Ambient Vibration Data

He, Xianfei; Moaveni, Babak; Conte, Joseph Le; Elgamal, Ahmed; Masri, Sami F.

doi:10.1111/j.1467-8667.2008.00544.x

Cited by 60 publications

(35 citation statements)

References 32 publications

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“…2007). Finally, 250 seconds vibration response of a cable-stayed bridge subject to wind-induced ambient vibration has been considered in (He et al 2008) to extract the modal parameters. Hence, according to the previously reported research works on OMA for bridge structures, it is obvious that our data acquisition setting i.e.…”

Section: Responsementioning

confidence: 99%

Automated Operational Modal Analysis of a Cable-Stayed Bridge

2017

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11Automated techniques for analyzing the dynamic behavior of full-scale civil structures are becoming 12 increasingly important for continuous structural health monitoring applications. This paper aims to extract the 13 structural modal parameters of a full-scale cable-stayed bridge from the collected 'output-only' vibration data 14 without the need of any user interactions. The work focuses on the development of an automated and robust 15 operational modal analysis (OMA) algorithm, utilizing a multi-stage clustering approach. The main 16 contribution of the work is to define a novel way of automatically defining the hierarchical clustering threshold 17 to enable the accurate identification of a complete set of modal parameters. The proposed algorithm is 18 demonstrated to work with any parametric system identification algorithm that uses the system order 'n' as the 19 sole parameter. In particular the results from Covariance-driven Stochastic Subspace Identification (SSI-Cov) 20 methods are presented. 21

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Section: Responsementioning

confidence: 99%

Automated Operational Modal Analysis of a Cable-Stayed Bridge

2017

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“…tive model that adequately captures the important dynamic behavior of the system. In engineering, this process is called system identification, an area of research that spans more than 40 years and which continues to receive much attention; for example, over the last few years in this journal alone, there have been numerous publications on system identification (e.g., Jiang and Adeli, 2005;Ching et al, 2006;Xu et al, 2007;Ni et al, 2008;He et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

Calculation of Posterior Probabilities for Bayesian Model Class Assessment and Averaging from Posterior Samples Based on Dynamic System Data

Cheung

Beck

2010

Computer-Aided Civil and Infrastructure Engineering

140

107

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In recent years, Bayesian model updating techniques based on dynamic data have been applied in system identification and structural health monitoring. Because of modeling uncertainty, a set of competing candidate model classes may be available to represent a system and it is then desirable to assess the plausibility of each model class based on system data. Bayesian model class assessment may then be used, which is based on the posterior probability of the different candidates for representing the system. If more than one model class has significant posterior probability, then Bayesian model class averaging provides a coherent mechanism to incorporate all of these model classes in making probabilistic predictions for the system response. This Bayesian model assessment and averaging requires calculation of the evidence for each model class based on the system data, which requires the evaluation of a multi-dimensional integral involving the product of the likelihood and prior defined by the model class. In this article, a general method for calculating the evidence is proposed based on using posterior samples from any Markov Chain Monte Carlo algorithm. The effectiveness of the proposed method is illustrated by Bayesian model updating and assessment using simulated earthquake data from a ten-story nonclassically damped building responding linearly and a four-story building responding inelastically.

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“…In case of complex structural systems it can be difficult to deal with the huge quantity of data coming from the monitoring process and various soft computing techniques have shown to be effective tools for data processing (Adeli and Jiang, 2006;Carden and Brownjohn, 2008;He et al, 2008;Jiang and Adeli, 2008a).…”

Section: Introductionmentioning

confidence: 99%

Soft Computing Based Multilevel Strategy for Bridge Integrity Monitoring

Arangio

Bontempi

2010

Computer-Aided Civil and Infrastructure Engineering

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In recent years, structural integrity monitoring has become increasingly important in structural engineering and construction management. It represents an important tool for the assessment of the dependability of existing complex structural systems as it integrates, in a unified perspective, advanced engineering analyses and experimental data processing. In the first part of this work the concepts of dependability and structural integrity are discussed and it is shown that an effective integrity assessment needs advanced computational methods. For this purpose, soft computing methods have shown to be very useful. In particular, in this work the neural networks model is chosen and successfully improved by applying the Bayesian inference at four hierarchical levels: for training, optimization of the regularization terms, data-based model selection, and evaluation of the relative importance of different inputs. In the second part of the article, Bayesian neural networks are..

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Modal Identification Study of Vincent Thomas Bridge Using Simulated Wind‐Induced Ambient Vibration Data

Cited by 60 publications

References 32 publications

Automated Operational Modal Analysis of a Cable-Stayed Bridge

Automated Operational Modal Analysis of a Cable-Stayed Bridge

Calculation of Posterior Probabilities for Bayesian Model Class Assessment and Averaging from Posterior Samples Based on Dynamic System Data

Soft Computing Based Multilevel Strategy for Bridge Integrity Monitoring

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