2023
DOI: 10.1115/1.4062475
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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 parameter 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 artifi… Show more

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Cited by 5 publications
(2 citation statements)
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“…Therefore, the propagation velocity of the excitation wave is only dependent on the real-time water depth of the wave. In future studies, we aim to explore the relationships between these influencing factors and other physical parameters, such as the speed of wave propagation, using the effective and accurate method of machine learning algorithms [45].…”
Section: Analyze the Changes In Propagation Velocity Of Excitation Wa...mentioning
confidence: 99%
“…Therefore, the propagation velocity of the excitation wave is only dependent on the real-time water depth of the wave. In future studies, we aim to explore the relationships between these influencing factors and other physical parameters, such as the speed of wave propagation, using the effective and accurate method of machine learning algorithms [45].…”
Section: Analyze the Changes In Propagation Velocity Of Excitation Wa...mentioning
confidence: 99%
“…Saha et al [28] proposed a machine learning-based method to predict the damage level of rubble-mound breakwaters. In this research work, machine learning algorithms such as Random Forest, Adaboost, Support vector regression, gradient boosting, and deep artificial neural networks were utilized to compare the performance to estimate the damage level of breakwaters.…”
Section: Literature Reviewmentioning
confidence: 99%