FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3190276984> ?p ?o ?g. }

• W3190276984 abstract "Abstract Optimal route selection for the subsea pipeline is a critical task for the pipeline design process, and the route selected can significantly affect the overall project cost. Therefore, it is necessary to design the routes to be economical and safe. On-bottom stability (OBS) and fixed obstacles like existing crossings and free spans are the main factors that affect the route selection. This article proposes a novel hybrid optimization method based on a typical Machine Learning algorithm for designing an optimal pipeline route. The proposed optimal route design is compared with one of the popular multi-objective optimization method named Genetic Algorithm (GA). The proposed pipeline route selection method uses a Reinforcement Learning (RL) algorithm, a particular type of machine learning method to train a pipeline system that would optimize the route selection of subsea pipelines. The route optimization tool evaluates each possible route by incorporating Onbottom stability criteria based on DNVGL-ST-109 standard and other constraints such as the minimum pipeline route length, static obstacles, pipeline crossings, and free-span section length. The cost function in the optimization method simultaneously handles the minimization of length and cost of mitigating procedures. Genetic Algorithm, a well established optimization method, has been used as a reference to compare the optimal route with the result from the proposed Reinforcement Learning based optimization method. Three different case studies are performed for finding the optimal route selection using the Reinforcement Learning (RL) approach considering the OBS criteria into its cost function and compared with the Genetic Algorithm (GA). The RL method saves upto 20% pipeline length for a complex problem with 15 crossings and 31 free spans. The RL optimization method provides the optimal routes, considering different aspects of the design and the costs associated with the various factors to stabilize a pipeline (mattress, trenching, burying, concrete coating, or even employing a more massive pipe with additional steel wall thickness). OBS criteria significantly influence the best route, indicating that the tool can reduce the pipeline's design time and minimize installation and operational costs of the pipeline. Conventionally the pipeline route optimization is performed by a manual process where the minimum roule length and static obstacles are considered to find an optimum route. The engineering is then performed to fulfill the criteria of this route, and this approach may not lead to an optimized engineering cost. The proposed Reinforced Learning method for route optimization is a mixed type, faster, and cost-efficient approach. It significantly minimizes the pipeline's installation and operational costs up to 20% of the conventional route selection process." @default.
• W3190276984 created "2021-08-16" @default.
• W3190276984 creator A5062160920 @default.
• W3190276984 date "2021-08-09" @default.
• W3190276984 modified "2023-10-16" @default.
• W3190276984 title "Machine Learning-Based Optimization for Subsea Pipeline Route Design" @default.
• W3190276984 cites W1967215663 @default.
• W3190276984 cites W2574321413 @default.
• W3190276984 cites W2591567476 @default.
• W3190276984 cites W2791974488 @default.
• W3190276984 cites W2891816981 @default.
• W3190276984 cites W4214717370 @default.
• W3190276984 doi "https://doi.org/10.4043/31031-ms" @default.
• W3190276984 hasPublicationYear "2021" @default.
• W3190276984 type Work @default.
• W3190276984 sameAs 3190276984 @default.
• W3190276984 citedByCount "1" @default.
• W3190276984 countsByYear W31902769842023 @default.
• W3190276984 crossrefType "proceedings-article" @default.
• W3190276984 hasAuthorship W3190276984A5062160920 @default.
• W3190276984 hasConcept C112972136 @default.
• W3190276984 hasConcept C119857082 @default.
• W3190276984 hasConcept C126255220 @default.
• W3190276984 hasConcept C127413603 @default.
• W3190276984 hasConcept C147764199 @default.
• W3190276984 hasConcept C154945302 @default.
• W3190276984 hasConcept C175309249 @default.
• W3190276984 hasConcept C199104240 @default.
• W3190276984 hasConcept C199360897 @default.
• W3190276984 hasConcept C2777737062 @default.
• W3190276984 hasConcept C33923547 @default.
• W3190276984 hasConcept C41008148 @default.
• W3190276984 hasConcept C43521106 @default.
• W3190276984 hasConcept C81917197 @default.
• W3190276984 hasConcept C87717796 @default.
• W3190276984 hasConcept C8880873 @default.
• W3190276984 hasConcept C97541855 @default.
• W3190276984 hasConceptScore W3190276984C112972136 @default.
• W3190276984 hasConceptScore W3190276984C119857082 @default.
• W3190276984 hasConceptScore W3190276984C126255220 @default.
• W3190276984 hasConceptScore W3190276984C127413603 @default.
• W3190276984 hasConceptScore W3190276984C147764199 @default.
• W3190276984 hasConceptScore W3190276984C154945302 @default.
• W3190276984 hasConceptScore W3190276984C175309249 @default.
• W3190276984 hasConceptScore W3190276984C199104240 @default.
• W3190276984 hasConceptScore W3190276984C199360897 @default.
• W3190276984 hasConceptScore W3190276984C2777737062 @default.
• W3190276984 hasConceptScore W3190276984C33923547 @default.
• W3190276984 hasConceptScore W3190276984C41008148 @default.
• W3190276984 hasConceptScore W3190276984C43521106 @default.
• W3190276984 hasConceptScore W3190276984C81917197 @default.
• W3190276984 hasConceptScore W3190276984C87717796 @default.
• W3190276984 hasConceptScore W3190276984C8880873 @default.
• W3190276984 hasConceptScore W3190276984C97541855 @default.
• W3190276984 hasLocation W31902769841 @default.
• W3190276984 hasOpenAccess W3190276984 @default.
• W3190276984 hasPrimaryLocation W31902769841 @default.
• W3190276984 hasRelatedWork W1554825793 @default.
• W3190276984 hasRelatedWork W1877099509 @default.
• W3190276984 hasRelatedWork W1973678997 @default.
• W3190276984 hasRelatedWork W2359237597 @default.
• W3190276984 hasRelatedWork W2473281553 @default.
• W3190276984 hasRelatedWork W26639490 @default.
• W3190276984 hasRelatedWork W2989254254 @default.
• W3190276984 hasRelatedWork W3086795060 @default.
• W3190276984 hasRelatedWork W4220935756 @default.
• W3190276984 hasRelatedWork W4280523339 @default.
• W3190276984 isParatext "false" @default.
• W3190276984 isRetracted "false" @default.
• W3190276984 magId "3190276984" @default.
• W3190276984 workType "article" @default.