Value of information-based risk and fatigue management for offshore structures

Agusta, Arifian; Leira, Bernt J.; Thöns, Sebastian

doi:10.1080/24705314.2020.1729659

Cited by 10 publications

(4 citation statements)

References 14 publications

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“…In the literature, deterministic (normalized) cost models are generally utilized in probabilistic reliability or risk-informed optimizations of I&M strategies (Agusta et al, 2020;Ali et al, 2020;60 Ambuhl and Sorensen, 2017;Bismut and Straub, 2021;Farhan et al, 2021;Florian and Sorensen, 2017;Long et al, 2020;Madsen et al, 1991;Martinez-Luengo and Shafiee, 2019;Moan, 2005;Nielsen and Sorensen, 2010;Pozzi and Kiureghian, 2011;Schneider et al, 2019;Straub, 2004;Thöns, 2018). Although such cost models enable an optimization of I&M activities, they lack the ability to fully quantify the effect of the uncertainties in the I&M costs 65 on the total expected costs of an I&M strategy.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic cost modeling as a basis for optimizing the inspection and maintenance of support structures in offshore wind farms

Farhan,

Schneider,

Thöns

et al. 2024

Preprint

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Abstract. The operational management of offshore wind farms includes inspection and maintenance (I&M) of the turbine support structures. These activities are complex and influenced by numerous uncertain factors that affect their costs. The uncertainty in the I&M costs should be considered in decision and value of information analyses performed to optimize I&M regimes. In this paper, we present a probabilistic cost model for I&M activities in an offshore wind farm serviced by boats operating from a port base. The model is developed based on interviews with a wind farm operator, consultants, and operation and maintenance engineers, as well as on scientific literature. Various I&M methods are considered, and the model is evaluated to predict probabilistic I&M costs at different levels, i.e., wind farm, structural system, and structural component. A sensitivity analysis is performed to study the influence of the different model parameters on the overall I&M costs. Finally, the model is included in a numerical example in which the I&M regime for a steel frame subject to fatigue is optimized using risk-informed methods. The frame's characteristics are comparable to those of a jacket structure supporting an offshore wind turbine. In the example, we demonstrate that the I&M costs can be considered deterministically as expected values since they are included in the optimization on a linear basis.

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Section: Introductionmentioning

confidence: 99%

Probabilistic cost modeling as a basis for optimizing the inspection and maintenance of support structures in offshore wind farms

Farhan,

Schneider,

Thöns

et al. 2024

Preprint

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show abstract

Section: Introductionmentioning

confidence: 99%

Comment on wes-2023-176

2024

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“…[5][6][7][8][9][10][11][12] For this reason, previous fatigue research focused on characterizing the strength degradation of concrete and steel materials under fatigue depending on various stress level and loading frequency. [13][14][15][16][17][18] These existing studies reveal that there are inter-related mechanisms among key variables including the compressive strength, aspect ratio of test specimen, maximum stress level, minimum stress to maximum stress ratio, and fatigue loading frequency. In addition, it is very hard to justify how much service life (or residual strength) remains after fatigue damage even though extensive fatigue test results are available.…”

Section: Introductionmentioning

confidence: 99%

“…The internal resistance mechanism in an RC member basically originates from two components: compressive resistance provided by concrete and its counterpart provided by reinforcements in tension 5–12 . For this reason, previous fatigue research focused on characterizing the strength degradation of concrete and steel materials under fatigue depending on various stress level and loading frequency 13–18 . These existing studies reveal that there are inter‐related mechanisms among key variables including the compressive strength, aspect ratio of test specimen, maximum stress level, minimum stress to maximum stress ratio, and fatigue loading frequency.…”

Section: Introductionmentioning

confidence: 99%

Residual strength of concrete subjected to fatigue based on machine learning technique

Zhang

Lee

et al. 2021

Structural Concrete

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This study utilizes the machine learning technique to solve the complex fatigue problem of concrete materials. To this end, several learning algorithms were addressed including the random forest (RF), support vector machine (SVM), and artificial neural networks (ANNs) models. Extensive experimental data were collected from literature to train the machine learning models for estimating the maximum number of cycles at failure (i.e., the so-called fatigue life). A machine learning model providing the best correlation was chosen through verifications. On this basis, a strength degradation model of concrete under fatigue loading was finally proposed to evaluate the residual strength of concrete after fatigue damage, which is a key factor in determining the remaining service life of concrete structures.

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Value of information-based risk and fatigue management for offshore structures

Cited by 10 publications

References 14 publications

Probabilistic cost modeling as a basis for optimizing the inspection and maintenance of support structures in offshore wind farms

Probabilistic cost modeling as a basis for optimizing the inspection and maintenance of support structures in offshore wind farms

Comment on wes-2023-176

Residual strength of concrete subjected to fatigue based on machine learning technique

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