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• W4367838145 abstract "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." @default.
• W4367838145 created "2023-05-04" @default.
• W4367838145 creator A5059314573 @default.
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• W4367838145 date "2023-05-24" @default.
• W4367838145 modified "2023-09-27" @default.
• W4367838145 title "An Application of Machine Learning Algorithms on the Prediction of the Damage Level of Rubble-Mound Breakwaters" @default.
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• W4367838145 doi "https://doi.org/10.1115/1.4062475" @default.
• W4367838145 hasPublicationYear "2023" @default.
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