Damage detection with interval analysis for uncertainties quantification

Liu, Gang; Mao, Zhu; Jun, Luo

doi:10.14264/uql.2016.922

Cited by 4 publications

(3 citation statements)

References 13 publications

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“…The material properties of the bridge components, such as the modulus of elasticity and Poisson's ratio of the bridge materials, may vary due to manufacturing variability or aging. This can introduce uncertainties in the strain-based [27] damage detection results.…”

Section: Materials Variabilitymentioning

confidence: 99%

Uncertainties in the Synthetic Data Generation for the Creation of Bridge Digital Twins

Jiménez Rios,

Plevris,

Nogal

2023

Proceedings of the 5th International Conference on Uncertainty Quantification in Computational Sciences and Engineering (UNCECO

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Digital twins (DTs) are virtual replicas of physical assets that can be used to monitor and manage their performance. To date, the DT concept has been effectively implemented in various industries, including aeronautics, manufacturing, medicine, and more recently, in the architecture, engineering, and construction sector. In the latter, these assets can be related to buildings, bridges, or other important infrastructures of the built environment. Although the creation of synthetic benchmark datasets for the validation of novel damage detection approaches has been attempted in the past, such alternatives are not easily findable or accessible. Thus, a new synthetic data generation framework is proposed within the DT paradigm context, that can produce FAIR benchmark databases that are characterized by Findability, Accessibility, Interoperability, and Reuse. This paper aims at exploring the uncertainty types, sources, and quantification approaches involved in the synthetic data generation methodologies and tools of the intended framework which could be used as a faster and cheaper alternative to real monitoring, for the creation and development of DT prototypes of bridges for both industry and research-oriented purposes. This work also highlights the benefits and drawbacks of implementing synthetic data for these purposes and points out tentative future improvements in the field.

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Section: Materials Variabilitymentioning

confidence: 99%

Uncertainties in the Synthetic Data Generation for the Creation of Bridge Digital Twins

Jiménez Rios,

Plevris,

Nogal

2023

Proceedings of the 5th International Conference on Uncertainty Quantification in Computational Sciences and Engineering (UNCECO

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“…Mustapha et al 13 utilized the AR-ARX model and the variation between the residual errors obtained from the intact and damaged states to detect cracking in steel reinforced concrete structures. Liu et al 14 considered the model coefficient sensitivity to the damage, and utilized the coefficient of time-domain model and Mahalanobis distance to identify structural damage.…”

Section: Introductionmentioning

confidence: 99%

Structural nonlinear damage identification using penalty conversion index of GARCH model

Guo

Heng

2020

Advances in Mechanical Engineering

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In the service period, some engineering structures may have cracks or other nonlinear damages. The nonlinear damages are the major influence to the safety of engineering structures, which should be detected as early as possible. Currently, the effective nonlinear damage detection method is still lacking. Therefore, a penalty conversion index based on generalized autoregressive conditional heteroskedasticity (GARCH) model is presented to identify the nonlinear damage. First, an exact expression of GARCH model is described, the bilinear stiffness characteristic of nonlinear damage is given, and acceleration responses are used to establish the GARCH model. Then, through the GARCH model analysis of nonlinear damages responses, it can be found that the variance of conditional variance are sensitive to the nonlinear damage information of acceleration responses, so a basic conversion index based on the variance of conditional variance is proposed. Finally, a penalty conversion index based on GARCH model is presented, which can reduce the interference induced by the adjacent unrelated factors. Numerical and experimental examples show that the identification results of the proposed penalty conversion index based on GARCH model are superior to those of the basic conversion index and the cepstral metric (CM) index.

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“…The principle of the method is that the distance between the models of damaged and undamaged states is correlated with the damage location information. Liu et al [15] proposed a non-probabilistic damage detection method, which employed the coefficient matrix of vector auto-regressive model to extract diagnosis elements and used Mahalanobis distance of these diagnosis elements to detect damages. Mei et al [12] presented a substructure-based damage identification method to find damage locations and severity, and autoregressive moving average with exogenous variables model residual was utilized to correct the damage indicator.…”

Section: Introductionmentioning

confidence: 99%

Structural nonlinear damage detection using improved Dempster-Shafer theory and time domain model

Guo¹,

Zhou²,

Zhang³

2019

J. vibroeng.

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In the service period, a crack may appear in some engineering structures. The development of accurate and effective methods for crack damage detection has become a topic of great importance. In this paper, a nonlinear damage detection method based on the improved Dempster-Shafer (D-S) theory and time domain model is presented. First, acceleration responses in the undamaged and damaged states are measured by using accelerometers. Then, acceleration responses are utilized to establish an autoregressive (AR) model, and residual time series of acceleration responses are used to establish an autoregressive conditional heteroskedasticity (ARCH) model. A cepstral metric conversion (CMC) method based on the AR model is employed to obtain local damage solution and an autoregressive conditional heteroskedasticity conversion (ARCHC) method based on ARCH model is presented to acquire another local damage solution. Finally, the D-S theory is applied to detect damages by integrating these local damage solutions, and an improved D-S theory is further presented to enhance the detection accuracy. The numerical and experimental examples show that the improved D-S theory has high detection accuracy and good performance.

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Damage detection with interval analysis for uncertainties quantification

Cited by 4 publications

References 13 publications

Uncertainties in the Synthetic Data Generation for the Creation of Bridge Digital Twins

Uncertainties in the Synthetic Data Generation for the Creation of Bridge Digital Twins

Structural nonlinear damage identification using penalty conversion index of GARCH model

Structural nonlinear damage detection using improved Dempster-Shafer theory and time domain model

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