An Application of Machine Learning Algorithms on the Prediction of the Damage Level of Rubble-Mound Breakwaters

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

Standard

An Application of Machine Learning Algorithms on the Prediction of the Damage Level of Rubble-Mound Breakwaters. / Saha, Susmita; De, Soumen; Changdar, Satyasaran.

I: Journal of Offshore Mechanics and Arctic Engineering, Bind 146, Nr. 1, 011202, 2024.

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

Harvard

Saha, S, De, S & Changdar, S 2024, 'An Application of Machine Learning Algorithms on the Prediction of the Damage Level of Rubble-Mound Breakwaters', Journal of Offshore Mechanics and Arctic Engineering, bind 146, nr. 1, 011202. https://doi.org/10.1115/1.4062475

APA

Saha, S., De, S., & Changdar, S. (2024). An Application of Machine Learning Algorithms on the Prediction of the Damage Level of Rubble-Mound Breakwaters. Journal of Offshore Mechanics and Arctic Engineering, 146(1), [011202]. https://doi.org/10.1115/1.4062475

Vancouver

Saha S, De S, Changdar S. An Application of Machine Learning Algorithms on the Prediction of the Damage Level of Rubble-Mound Breakwaters. Journal of Offshore Mechanics and Arctic Engineering. 2024;146(1). 011202. https://doi.org/10.1115/1.4062475

Author

Saha, Susmita ; De, Soumen ; Changdar, Satyasaran. / An Application of Machine Learning Algorithms on the Prediction of the Damage Level of Rubble-Mound Breakwaters. I: Journal of Offshore Mechanics and Arctic Engineering. 2024 ; Bind 146, Nr. 1.

Bibtex

@article{a532fa2ad6a54e32982dfbc66b05fd0c,
title = "An Application of Machine Learning Algorithms on the Prediction of the Damage Level of Rubble-Mound Breakwaters",
abstract = "The stability analysis of breakwaters is very important to have a safe and economic design of these coastal protective structures and the damage level is one of the most important parameters in this context. In the recent past, machine learning techniques showed immense potential in transforming many industries and processes, for making them more efficient and accurate. In this study, five advanced machine learning algorithms, support vector regression, random forest, Adaboost, gradient boosting, and deep artificial neural network, were employed and analyzed on estimation of the damage level of rubble-mound breakwaters. A large experimental dataset, considering almost every stability variable with their whole ranges, was used in this purpose. Also, a detailed feature analysis is presented to have an insight into the relations between these variables. It was found that the present study had overcome all of the limitations of existing studies related to this field and delivered the highest level of accuracy.",
keywords = "breakwater{\textquoteright}s stability, damage level, ensemble learning, feature analysis, machine learning, neural network, regression analysis",
author = "Susmita Saha and Soumen De and Satyasaran Changdar",
note = "Publisher Copyright: Copyright {\textcopyright} 2023 by ASME.",
year = "2024",
doi = "10.1115/1.4062475",
language = "English",
volume = "146",
journal = "Journal of Offshore Mechanics and Arctic Engineering",
issn = "0892-7219",
publisher = "The American Society of Mechanical Engineers(ASME)",
number = "1",

}

RIS

TY - JOUR

T1 - An Application of Machine Learning Algorithms on the Prediction of the Damage Level of Rubble-Mound Breakwaters

AU - Saha, Susmita

AU - De, Soumen

AU - Changdar, Satyasaran

N1 - Publisher Copyright: Copyright © 2023 by ASME.

PY - 2024

Y1 - 2024

N2 - The stability analysis of breakwaters is very important to have a safe and economic design of these coastal protective structures and the damage level is one of the most important parameters in this context. In the recent past, machine learning techniques showed immense potential in transforming many industries and processes, for making them more efficient and accurate. In this study, five advanced machine learning algorithms, support vector regression, random forest, Adaboost, gradient boosting, and deep artificial neural network, were employed and analyzed on estimation of the damage level of rubble-mound breakwaters. A large experimental dataset, considering almost every stability variable with their whole ranges, was used in this purpose. Also, a detailed feature analysis is presented to have an insight into the relations between these variables. It was found that the present study had overcome all of the limitations of existing studies related to this field and delivered the highest level of accuracy.

AB - The stability analysis of breakwaters is very important to have a safe and economic design of these coastal protective structures and the damage level is one of the most important parameters in this context. In the recent past, machine learning techniques showed immense potential in transforming many industries and processes, for making them more efficient and accurate. In this study, five advanced machine learning algorithms, support vector regression, random forest, Adaboost, gradient boosting, and deep artificial neural network, were employed and analyzed on estimation of the damage level of rubble-mound breakwaters. A large experimental dataset, considering almost every stability variable with their whole ranges, was used in this purpose. Also, a detailed feature analysis is presented to have an insight into the relations between these variables. It was found that the present study had overcome all of the limitations of existing studies related to this field and delivered the highest level of accuracy.

KW - breakwater’s stability

KW - damage level

KW - ensemble learning

KW - feature analysis

KW - machine learning

KW - neural network

KW - regression analysis

UR - http://www.scopus.com/inward/record.url?scp=85176793325&partnerID=8YFLogxK

U2 - 10.1115/1.4062475

DO - 10.1115/1.4062475

M3 - Journal article

AN - SCOPUS:85176793325

VL - 146

JO - Journal of Offshore Mechanics and Arctic Engineering

JF - Journal of Offshore Mechanics and Arctic Engineering

SN - 0892-7219

IS - 1

M1 - 011202

ER -

ID: 374120894