Application of large-scale non-Gaussian stochastic fields for the study of corrosion-induced structural deterioration

Shafei, Behrouz; Alipour, Alice

doi:10.1016/j.engstruct.2014.12.024

Cited by 51 publications

(16 citation statements)

References 41 publications

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“…Spatial distribution of pits along the deteriorating reinforced concrete columns may also be modeled by using stochastic field theory by generating multidimensional Gaussian or non-Gaussian (depending on the base distribution of variables) random fields. The successful application of such methods have been demonstrated for rebar corrosion in reinforced concrete columns and decks of highway bridges an decks of port wharves [52,61,62]. An extension of this work in future would include such techniques based on random field theory along with a comparative assessment of the extent of deterioration and time-dependent vulnerability evaluation of bridges based on the current assumption.…”

Section: Analytical Modeling Of Pristine and Deteriorating Rc Columnsmentioning

confidence: 92%

Consideration of time-evolving capacity distributions and improved degradation models for seismic fragility assessment of aging highway bridges

Ghosh

Sood

2016

Reliability Engineering & System Safety

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Section: Analytical Modeling Of Pristine and Deteriorating Rc Columnsmentioning

confidence: 92%

Consideration of time-evolving capacity distributions and improved degradation models for seismic fragility assessment of aging highway bridges

Ghosh

Sood

2016

Reliability Engineering & System Safety

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“…Shafei and Alipour (2015) [112] introduced the application of large-scale stochastic fields to study the spatial-temporal variability of main parameters contributing to the corrosion of concrete structures. To take probabilistic aspects of the corrosion process into account, several large-scale Gaussian and non-Gaussian random fields denoting structural characteristics, material prop- Fig.…”

Section: Structural Materials In Civil Engineeringmentioning

confidence: 99%

Advances in Gaussian random field generation: a review

Liu

Sun

et al. 2019

Comput Geosci

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Gaussian (normal) distribution is a basic continuous probability distribution in statistics, it plays a substantial role in scientific and engineering problems that related to stochastic phenomena. This paper aims to review state of the art of Gaussian random field generation methods, their applications in scientific and engineering issues of interest, and open-source software/packages for Gaussian random field generation. To this end, first we briefly introduce basic mathematical concepts and theories in Gaussian random field, then seven commonly-used Gaussian random field generation methods are systematically presented. The basic idea, mathematical framework of each generation method are introduced in detail and comparisons of these methods are summarized. Then representative applications of Gaussian random field in various areas, especially of engineering interest in recent two decades, are reviewed. For readers' convenience, four representative example codes are provided, and several relevant up-to-date open-source software and packages that freely available from the Internet are introduced.

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“…A common way to deal with such uncertainties is to use a probabilistic framework by modeling the input data with random fields [2,10,19,21,34].…”

Section: Introductionmentioning

confidence: 99%

Modeling time and spatial variability of degradation through gamma processes for structural reliability assessment

2019

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The objective of this work is to propose a spatio-temporal random field for assessing the effect of spatial variability on the degradation process in structural reliability assessment. Our model extends the classical Gamma process which is usually developed for degradation temporal variability concerns to integrate spatial variability and heterogeneity issues. A log-normal distributed spatial random scale is then introduced in the Gamma process. Mathematical models for a structure degradation in space and time and estimation procedures are developed in this paper. Approximations and simulations are given to evaluate the failure time distribution and to characterize the residual lifetime after inspection. Simulation results are performed using pseudo-random data based on Monte-Carlo simulations to fit the model and the inference of their parameters. The two proposed estimation method-Method of Moments and Pseudo Maximum Likelihood-are numerically compared according to statistical measures. The accuracy of the methods are also discussed by numerical examples given the approximations of quantities of interests.

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Application of large-scale non-Gaussian stochastic fields for the study of corrosion-induced structural deterioration

Cited by 51 publications

References 41 publications

Consideration of time-evolving capacity distributions and improved degradation models for seismic fragility assessment of aging highway bridges

Consideration of time-evolving capacity distributions and improved degradation models for seismic fragility assessment of aging highway bridges

Advances in Gaussian random field generation: a review

Modeling time and spatial variability of degradation through gamma processes for structural reliability assessment

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