Collision failure risk analysis of falling object on subsea pipelines based on machine learning scheme
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- @Article{JIANG:2020:EFA,
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author = "Fengyuan Jiang and Sheng Dong",
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title = "Collision failure risk analysis of falling object on
subsea pipelines based on machine learning scheme",
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journal = "Engineering Failure Analysis",
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volume = "114",
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pages = "104601",
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year = "2020",
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ISSN = "1350-6307",
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DOI = "doi:10.1016/j.engfailanal.2020.104601",
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URL = "http://www.sciencedirect.com/science/article/pii/S1350630720302855",
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keywords = "genetic algorithms, genetic programming, Offshore
pipelines, Quantitative risk analysis, Machine learning
algorithm, Impact loading, Pipe-soil interaction",
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abstract = "Platform falling object collision on offshore
pipelines are catastrophic to the environment and
economy. Based on finite element analysis and machine
learning algorithms, a quantitative analysis model is
proposed to quantify failure risk. To consider the
uncertainties and nonlinear effects in the collision
events, the Latin Hypercube Sampling technique and the
finite element simulation is coupled to draw the sample
space. Then four machine learning models are developed
and the prediction abilities in the pipeline response
are compared. The genetic programming shows the best
performance with the relative absolute error of
0.04-0.05, which is integrated into Monte Carlo
Simulation to complete the risk analysis. This
quantitative analysis model is verified with a method
and indicates good consistency and potential in
considering nonlinear effects and pipe-soil
interactions. Effects of related factors on failure
risk are examined, including seabed flexibility, burial
depth, acceptable criterion, and sensibility of basic
variables. Compared with the method recommended by the
Det Norkske Veritas, the proposed model can account for
the seabed flexibility effect, and the failure risk
declined by 23.6percent. The increase in burial depth
affects risk reduction significantly but is limited
under a strict criterion. The fitting equations of
burial depth and failure probabilities as well as
different acceptable criteria are proposed for safety
design. Sensibility analysis of the basic variables
reveals that the quality of wall thickness and pipeline
diameter are important to failure risk",
- }
Genetic Programming entries for
Fengyuan Jiang
Sheng Dong
Citations