Bayesian damage identification based on autoregressive model and MH-PSO hybrid MCMC sampling method

Luo, Jin; Huang, Minshui; Xiang, Chunyan; Lei, Yongzhi

doi:10.1007/s13349-021-00541-5

Cited by 20 publications

(10 citation statements)

References 36 publications

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“…The applicability of this method to damage assessment was verified in the case analysis of a bridge hanger. Luo et al [183] introduced the PSO algorithm into the MH sampling method, and called it the MH-PSO hybrid MCMC sampling method. Numerical damage recognition results showed that the method had enhanced sampling efficiency and damage recognition ability.…”

Section: Bayesian Methodsmentioning

confidence: 99%

The Current Development of Structural Health Monitoring for Bridges: A Review

et al. 2023

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The health monitoring system of a bridge is an important guarantee for the safe operation of the bridge and has always been a research hotspot in the field of civil engineering. This paper reviews the latest progressions in bridge health monitoring over the past five years. This paper is organized according to the various links of the bridge health monitoring system. Firstly, the literature on monitoring technology is divided into two categories, sensor technology and computer vision technology, for review. Secondly, based on the obtained monitoring data, the data processing methods including preprocessing, noise reduction, and reconstruction are summarized. Then, the technical literature on abnormal data early warning systems is summarized. The recent advances in vibration-based and non-destructive testing-based damage identification methods are reviewed in the next section. Finally, the advantages and disadvantages of the existing research and the future research directions are summarized. This review aims to provide a clear framework and some reliable methods for future research.

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Section: Bayesian Methodsmentioning

confidence: 99%

The Current Development of Structural Health Monitoring for Bridges: A Review

et al. 2023

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“…Two experimental examples with strong nonlinearities, i.e., a 2-D steel frame building and a 3-D isolated bridge, were used to validate the method, with results showing that the proposed method could be used to calibrate large and complex hysteretic FE models. Luo et al [107] focused on the improvement of the MCMC sampling method and obtaining damage identification accuracy, proposing a novel and effective sampling method called the MH-PSO hybrid MCMC sampling method. The damage identification poster PDF objective function was also constructed based on the autoregressive model, and the related work was able to enhance the damage identification accuracy greatly.…”

Section: Bayesian Inference-based Model Updating Methodsmentioning

confidence: 99%

Temperature Effect on Vibration Properties and Vibration-Based Damage Identification of Bridge Structures: A Literature Review

2022

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In civil engineering structures, modal changes produced by environmental conditions, especially temperature, can be equivalent to or greater than the ones produced by damage. Therefore, it is necessary to distinguish the variations in structural properties caused by environmental changes from those caused by structural damages. In this paper, we present a review of the technical literature concerning variations in the vibration properties of civil structures under varying temperature conditions and damage identification methods for bridge structures. First, the literature on the effect of temperature on vibration properties is roughly divided into experimental and theoretical studies. According to the classification of theoretical research methods, the progress in research on the probability analysis method, the artificial intelligence method, and the optimization algorithm method in this field is reviewed. Based on the different methods of experimental research employed in this field, the experimental research is reviewed according to qualitative and quantitative analyses. Then, damage identification methods for bridge structures are reviewed, considering data-based and model-based methods. Finally, different research methods are summarized.

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“…Due to its good time series fitting ability, AR coefficients are usually employed to analyze dynamic signals (Luo et al, 2022; Svendsen et al, 2022). Therefore, it is feasible to identify structural damage by constructing a damage index with AR coefficients.…”

Section: Structural Damage Identification Based On Nmfbi-ar and Cnnmentioning

confidence: 99%

“…The thermal coefficient of the structure is 23.6 × 10 −6 /°C. Then, the particle swarm optimization (PSO) algorithm (Luo et al, 2022) is utilized to update the model based on the measured frequencies and mode shapes in the intact state. The updated results about frequencies and MACs of the experimental and numerical model are summarized in Table 5.…”

Section: Application Casesmentioning

confidence: 99%

Structural damage identification under ambient temperature variations based on CNN and normalized modal flexibility-autoregressive coefficients hybrid index

Gu,

Xiang,

Luo

et al. 2024

Advances in Structural Engineering

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This study proposes a novel method to identify structural damage considering ambient temperature variations. In this method, the normalized modal flexibility based index (nMFBI) and autoregressive (AR) coefficients are combined to form the nMFBI-AR hybrid index, and convolutional neural networks (CNN) are exploited to locate and quantify the damage. Moreover, the effects of ambient temperature variations and measurement noise are not considered in the training dataset, which is preferable in practical engineering. To verify the effectiveness of the proposed method, firstly, a numerical structure of a simply supported beam is investigated, and the performance of the nMFBI-AR index is evaluated by making a comparison with the nMFBI and AR indexes. Then, the proposed method is further verified by a test model of a three-story frame and a practical engineering example of a continuous rigid frame bridge. The results demonstrate that although the influence of ambient temperature variations and measurement noise are only considered in the test datasets, this approach has the best performance in locating and quantifying structural damage, and the errors are less than 16%, which is promising. In addition, this paper provides a guideline and a new idea for the study of damage index based on time-frequency hybrid information.

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Bayesian damage identification based on autoregressive model and MH-PSO hybrid MCMC sampling method

Cited by 20 publications

References 36 publications

The Current Development of Structural Health Monitoring for Bridges: A Review

The Current Development of Structural Health Monitoring for Bridges: A Review

Temperature Effect on Vibration Properties and Vibration-Based Damage Identification of Bridge Structures: A Literature Review

Structural damage identification under ambient temperature variations based on CNN and normalized modal flexibility-autoregressive coefficients hybrid index

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