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Reliability analysis of mooring lines for floating structures using ANN-BN inference
https://orcid.org/0000-0001-7669-0878
The harsh marine environment is a significant threat to the safety of floating structure systems. To address this, mooring systems have seen widespread application as an important component in the stabilization of floating structures. This article proposes a methodology to assess the reliability of mooring lines under given extreme environmental conditions based on artificial neural network–Bayesian network inference. Different types of artificial neural networks, including radial basis function neural networks and back propagation neural networks, are adopted to predict the extreme response of mooring lines according to a series of measured environmental data. A failure database under extreme sea conditions is then established in accordance with the failure criterion of mooring systems. There is a failure of mooring lines when the maximum tension exceeds the allowable breaking strength. Finally, the reliability analysis of moored floating structures under different load directions is conducted using Bayesian networks. To demonstrate the proposed methodology, the failure probability of a sample semi-submersible platform at a water depth of 1500 m is estimated. This approach utilizes artificial neural networks’ capacity for calculation efficiency and validates artificial neural networks for the response prediction of floating structures. Furthermore, it can also be employed to estimate the failure probability of other complex floating structures.
Reliability analysis of mooring lines for floating structures using ANN-BN inference
https://orcid.org/0000-0001-7669-0878
The harsh marine environment is a significant threat to the safety of floating structure systems. To address this, mooring systems have seen widespread application as an important component in the stabilization of floating structures. This article proposes a methodology to assess the reliability of mooring lines under given extreme environmental conditions based on artificial neural network–Bayesian network inference. Different types of artificial neural networks, including radial basis function neural networks and back propagation neural networks, are adopted to predict the extreme response of mooring lines according to a series of measured environmental data. A failure database under extreme sea conditions is then established in accordance with the failure criterion of mooring systems. There is a failure of mooring lines when the maximum tension exceeds the allowable breaking strength. Finally, the reliability analysis of moored floating structures under different load directions is conducted using Bayesian networks. To demonstrate the proposed methodology, the failure probability of a sample semi-submersible platform at a water depth of 1500 m is estimated. This approach utilizes artificial neural networks’ capacity for calculation efficiency and validates artificial neural networks for the response prediction of floating structures. Furthermore, it can also be employed to estimate the failure probability of other complex floating structures.
Reliability analysis of mooring lines for floating structures using ANN-BN inference
https://orcid.org/0000-0001-7669-0878
The harsh marine environment is a significant threat to the safety of floating structure systems. To address this, mooring systems have seen widespread application as an important component in the stabilization of floating structures. This article proposes a methodology to assess the reliability of mooring lines under given extreme environmental conditions based on artificial neural network–Bayesian network inference. Different types of artificial neural networks, including radial basis function neural networks and back propagation neural networks, are adopted to predict the extreme response of mooring lines according to a series of measured environmental data. A failure database under extreme sea conditions is then established in accordance with the failure criterion of mooring systems. There is a failure of mooring lines when the maximum tension exceeds the allowable breaking strength. Finally, the reliability analysis of moored floating structures under different load directions is conducted using Bayesian networks. To demonstrate the proposed methodology, the failure probability of a sample semi-submersible platform at a water depth of 1500 m is estimated. This approach utilizes artificial neural networks’ capacity for calculation efficiency and validates artificial neural networks for the response prediction of floating structures. Furthermore, it can also be employed to estimate the failure probability of other complex floating structures.
Reliability analysis of mooring lines for floating structures using ANN-BN inference
Zhao, Yuliang (Autor:in) / Dong, Sheng (Autor:in) / Jiang, Fengyuan (Autor:in)
01.02.2021
19 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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