Publication: A novel hybridized metaheuristic technique inenhancing the diagnosis of cross-sectional dentdamaged offshore platform members
Issued Date
2019
Resource Type
Language
eng
Rights
Mahidol University
Rights Holder(s)
John Wiley & Sons
Bibliographic Citation
Computational Intelligence.
Suggested Citation
Wonsiri Punurai, M.S. Azad, Nantiwat Pholdee, Sujin Bureerat, Chana Sinsabvarodom A novel hybridized metaheuristic technique inenhancing the diagnosis of cross-sectional dentdamaged offshore platform members. Computational Intelligence.. doi:10.1111/coin.12247 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/48014
Research Projects
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Thesis
Title
A novel hybridized metaheuristic technique inenhancing the diagnosis of cross-sectional dentdamaged offshore platform members
Other Contributor(s)
Mahidol University. Faculty of Engineering. Department of Civil and Environmental Engineering
Khon Kaen University. Faculty of Engineering. Sustainable and Infrastructure Research and Development Center
Norwegian University of Science and Technology. Faculty of Engineering. Department of Marine Technology
Khon Kaen University. Faculty of Engineering. Sustainable and Infrastructure Research and Development Center
Norwegian University of Science and Technology. Faculty of Engineering. Department of Marine Technology
Abstract
Offshore jacket platforms are widely used for oil and gas extraction as well as transportation in shallow to moderate water depth. Tubular cross-sectional elements are used to construct offshore platforms. Tubular cross-sections impart higher resistance against hydrodynamic forces and have high torsional rigidity. During operation, the members can be partially or fully damaged due to lateral impacts. The lateral impacts can be due to ship collisions or through the impact of falling objects. The impact forces can weaken some members that influence the overall performance of the platform. This demonstrates an urgent need to develop a framework that can accurately forecast dent depth as well as dent angle of the affected members. This study investigates the use of an adaptive metaheuristics algorithm to provide automatic detection of denting damage in an offshore structure. The damage information includes dent depth and the dent angle. A model is developed in combination with the percentage of the dent depth of the damaged member and is used to assess the performance of the method. It demonstrates that small changes in stiffness of individual damaged bracing members are detectable from measurements of global structural motion
Sponsorship
Thailand Research Fund; Mahidol University; H2020 Marie Skłodowska‐Curie Actions, 730888