HYDROSTATIC STABILITY EXPLORATION ON FLOATING STRUCTURES USING MACHINE LEARNING

Hamid S. ElDarwich; Krisna Adi Pawitan; Iman Mansouri; Maria M. Garlock

HYDROSTATIC STABILITY EXPLORATION ON FLOATING STRUCTURES USING MACHINE LEARNING

Hamid S. ElDarwich, Krisna Adi Pawitan, Iman Mansouri, Maria M. Garlock

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

A hydrostatic stability analysis is an important first step in designing floating structures. Most of the currently available commercial software is limited to hydrostatic stability curves. Current research tries to address this limitation, by developing a framework which couples numerical hydrostatic stability analysis based on potential energy minimization, with a machine learning (ML) model based on genetic programming (GP). In this way, potential energy functions are efficiently obtained. The resulting analytical formulations offer a wider understanding of the hydrostatic stability of floating structures.

Original language	English (US)
Title of host publication	Proceedings of the Coastal Engineering Conference
Editors	Daniel Cox
Publisher	American Society of Civil Engineers (ASCE)
Edition	37
ISBN (Electronic)	9780989661164
State	Published - Sep 1 2023
Event	37th International Conference on Coastal Engineering, ICCE 2022 - Sydney, Australia Duration: Dec 4 2022 → Dec 9 2022

Publication series

Name	Proceedings of the Coastal Engineering Conference
Number	37
ISSN (Print)	0161-3782

Conference

Conference	37th International Conference on Coastal Engineering, ICCE 2022
Country/Territory	Australia
City	Sydney
Period	12/4/22 → 12/9/22

All Science Journal Classification (ASJC) codes

Civil and Structural Engineering
Ocean Engineering
Oceanography

Cite this

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title = "HYDROSTATIC STABILITY EXPLORATION ON FLOATING STRUCTURES USING MACHINE LEARNING",

abstract = "A hydrostatic stability analysis is an important first step in designing floating structures. Most of the currently available commercial software is limited to hydrostatic stability curves. Current research tries to address this limitation, by developing a framework which couples numerical hydrostatic stability analysis based on potential energy minimization, with a machine learning (ML) model based on genetic programming (GP). In this way, potential energy functions are efficiently obtained. The resulting analytical formulations offer a wider understanding of the hydrostatic stability of floating structures.",

author = "ElDarwich, {Hamid S.} and Pawitan, {Krisna Adi} and Iman Mansouri and Garlock, {Maria M.}",

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ElDarwich, HS, Pawitan, KA, Mansouri, I & Garlock, MM 2023, HYDROSTATIC STABILITY EXPLORATION ON FLOATING STRUCTURES USING MACHINE LEARNING. in D Cox (ed.), Proceedings of the Coastal Engineering Conference. 37 edn, Proceedings of the Coastal Engineering Conference, no. 37, American Society of Civil Engineers (ASCE), 37th International Conference on Coastal Engineering, ICCE 2022, Sydney, Australia, 12/4/22.

HYDROSTATIC STABILITY EXPLORATION ON FLOATING STRUCTURES USING MACHINE LEARNING. / ElDarwich, Hamid S.; Pawitan, Krisna Adi; Mansouri, Iman et al.
Proceedings of the Coastal Engineering Conference. ed. / Daniel Cox. 37. ed. American Society of Civil Engineers (ASCE), 2023. (Proceedings of the Coastal Engineering Conference; No. 37).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AB - A hydrostatic stability analysis is an important first step in designing floating structures. Most of the currently available commercial software is limited to hydrostatic stability curves. Current research tries to address this limitation, by developing a framework which couples numerical hydrostatic stability analysis based on potential energy minimization, with a machine learning (ML) model based on genetic programming (GP). In this way, potential energy functions are efficiently obtained. The resulting analytical formulations offer a wider understanding of the hydrostatic stability of floating structures.

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HYDROSTATIC STABILITY EXPLORATION ON FLOATING STRUCTURES USING MACHINE LEARNING

Abstract

Publication series

Conference

All Science Journal Classification (ASJC) codes

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