Reliability-Based Design and Planning of Inspection and Monitoring of OffshoreWind Turbines

Dominguez, Sergio Marquez

doi:10.13052/rp-oa9788793102620

Cited by 2 publications

(3 citation statements)

References 24 publications

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“…ε is often assumed to have a Normal distributed [31]. The Likelihood function to be used to estimate the optimal values of the parameters K, m, and σ ε from test data is written [22]:…”

Section: Test Datamentioning

confidence: 99%

“…Therefore, the development of stochastic models for the fatigue limit state and estimation of the resulting reliability can be considered as a contribution to reliability assessment of these types of structures, with respect to fatigue failure and also as the basis for the development of optimal strategies for the maintenance of wind turbines and bridges. [22].…”

Section: Introductionmentioning

confidence: 99%

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Development of Stochastic Fatigue Model of Reinforcement for Reliability of Concrete Structures

et al. 2020

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This paper presents recent contributions to the Marie Skłodowska-Curie Innovative Training Network titled INFRASTAR (Innovation and Networking for Fatigue and Reliability Analysis of Structures-Training for Assessment of Risk) in the field of reliability approaches for decision-making for wind turbines and bridges . Stochastic modeling of uncertainties for fatigue strength parameters is an important step as a basis for reliability analyses. In this paper, the Maximum Likelihood Method (MLM) is used for fitting the statistical parameters in a regression model for the fatigue strength of reinforcement bars. Furthermore, application of the Bootstrapping method is investigated. The results indicate that the latter methodology does not work well in the considered case study because of run-out tests within the test data. Moreover, the use of the Bayesian inference with the Markov Chain Monto Carlo approach is studied. These results indicate that a reduction in the statistical uncertainty can be obtained, and thus, better parameter estimates are obtained. The results are used for stochastic modelling in reliability assessment of a case study with a composite bridge. The reduction in statistical uncertainty shows high impact on the fatigue reliability in a case study on the Swiss viaduct Crêt De l’Anneau.

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“…ε is often assumed to have a Normal distributed [31]. The Likelihood function to be used to estimate the optimal values of the parameters K, m, and σ ε from test data is written [22]:…”

Section: Test Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Development of Stochastic Fatigue Model of Reinforcement for Reliability of Concrete Structures

et al. 2020

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“…𝐾𝐾 and 𝑚𝑚 are fatigue parameters to be fitted by MLM here using test data [58] & [59]. A stochastic fatigue resistance model was proposed in by [60]; however, it includes only data from only one laboratory test, [58]. The uncertainties in fatigue life (𝑁𝑁) can be taken care of by an error term 𝜖𝜖 and equation (3)(4) can be rewritten as:…”

Section: Stochastic Fatigue Resistance Model (S-n) For Reinforcementmentioning

confidence: 99%

Fatigue Reliability of Concrete Elements in Bridges and Wind Turbines

Mankar¹

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Reliability-Based Design and Planning of Inspection and Monitoring of OffshoreWind Turbines

Cited by 2 publications

References 24 publications

Development of Stochastic Fatigue Model of Reinforcement for Reliability of Concrete Structures

Development of Stochastic Fatigue Model of Reinforcement for Reliability of Concrete Structures

Fatigue Reliability of Concrete Elements in Bridges and Wind Turbines

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