Applied machine learning model comparison: Predicting offshore platform integrity with gradient boosting algorithms and neural networks-Reference-Cited by-同舟云学术

Applied machine learning model comparison: Predicting offshore platform integrity with gradient boosting algorithms and neural networks

Published:2022-05 Issue: Volume:83 Page:103152
ISSN:0951-8339
Container-title:Marine Structures
language:en
Short-container-title:Marine Structures

Author:

Dyer Alec S.^ORCID,Zaengle Dakota^ORCID,Nelson Jake R.,Duran Rodrigo,Wenzlick Madison^ORCID,Wingo Patrick C.^ORCID,Bauer Jennifer R.^ORCID,Rose Kelly,Romeo Lucy^ORCID

Funder

Oak Ridge Institute for Science and Education

National Energy Technology Laboratory

U.S. Department of Energy

Publisher

Elsevier BV

Subject

Mechanical Engineering,Mechanics of Materials,Ocean Engineering,General Materials Science

Reference120 articles.

1. IHS Enerdeq® well header database,2015

2. Offshore production nearly 30% of global crude oil output in;Manning,2015-2016

3. Alternate uses of retired oil and gas platforms in the Gulf of Mexico;Kolian;Ocean Coast Manag,2019

4. Gulf operators continue to advance platform life extension projects;Beaubouef,2020

5. Reconversion of offshore oil and gas platforms into renewable energy sites production: assessment of different scenarios;Leporini;Renew Energy,2019

Cited by 21 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Structural integrity assessment of an offshore platform using RB-FEA;International Journal of Structural Integrity;2024-08-22

2. Extensive pipeline location data resource: Integrating reported incidents, past environmental loadings, and potential geohazards for integrity evaluations in the U.S. Gulf of Mexico;Data in Brief;2024-08

3. Bubble collapse patterns recognition and flow field prediction based on machine learning;Physics of Fluids;2024-08-01

4. Reducing CO2 Emissions through the Strategic Optimization of a Bulk Carrier Fleet for Loading and Transporting Polymetallic Nodules from the Clarion-Clipperton Zone;Energies;2024-07-10

5. Drilling Data Based Approach for Real-Time Rate of Penetration Prediction for Motorized Bottom Hole Assembly Using Artificial Intelligence;Day 2 Wed, May 08, 2024;2024-05-07