2018
DOI: 10.1155/2018/2719682
|View full text |Cite
|
Sign up to set email alerts
|

Neural Networks Applied to the Wave-Induced Fatigue Analysis of Steel Risers

Abstract: Time domain stochastic wave dynamic analyses of offshore structures are computationally expensive. Considering the wave-induced fatigue assessment for such structures, the combination of many environmental loading cases and the need of long time-series responses make the computational cost even more critical. In order to reduce the computational burden related to the wave-induced fatigue analysis of Steel Catenary Risers (SCRs), this work presents the application of a recently developed hybrid methodology that… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1

Citation Types

0
1
0

Year Published

2020
2020
2024
2024

Publication Types

Select...
6

Relationship

0
6

Authors

Journals

citations
Cited by 15 publications
(1 citation statement)
references
References 10 publications
0
1
0
Order By: Relevance
“…e past decades have seen a massive construction of marine structures, including offshore turbines [1], platforms, and subsea risers for exploring ocean resources. Of all these structures, steel catenary risers (SCRs), which are often pipeline structures with a large slender ratio [2,3], have been widely used in engineering practice to connect subsea production well and offshore platforms, as well as to convey production chemicals and hydrocarbons in oil and gas exploration.…”
Section: Introductionmentioning
confidence: 99%
“…e past decades have seen a massive construction of marine structures, including offshore turbines [1], platforms, and subsea risers for exploring ocean resources. Of all these structures, steel catenary risers (SCRs), which are often pipeline structures with a large slender ratio [2,3], have been widely used in engineering practice to connect subsea production well and offshore platforms, as well as to convey production chemicals and hydrocarbons in oil and gas exploration.…”
Section: Introductionmentioning
confidence: 99%