TY - JOUR

T1 - Prediction of the stability number of conventional rubble-mound breakwaters using machine learning algorithms

AU - Saha, Susmita

AU - Changdar, Satyasaran

AU - De, Soumen

N1 - Publisher Copyright: © 2022

PY - 2024

Y1 - 2024

N2 - An important issue in designing the structures of rubble-mound breakwaters, is to estimate the stability number of its armor block. Most of the traditional stability analysis methods are not compatible enough to handle the obscurities, indistintness and uncertainties of this field. The relations between stability number, damage level and other stability variables can better be modeled using the advanced techniques of machine learning (ML) algorithms. In this prospect, three new ML models consisting of two ensemble learning models; Random Forest, Gradient Boosting and one fully connected deep artificial neural network based prediction model have been presented in this study. Using the ensemble learning models a detailed feature analysis has been introduced here, to understand the feature importances of stability variables on the stability number. To the best of the author's knowledge, these have never been used in this field of stability analysis of rubble-mound breakwaters. Outperforming all of the conventional methods, the proposed study has delivered the highest level of accuracy as 99%, in the prediction of the stability number. Also, the proposed ML models are found to perform better, in dealing with the complex non-linearities related to this field. The feature analysis gives a meaningful insight into the dataset. Therefore, this study can be a useful alternative approach for the designers of the rubble-mound breakwaters.

AB - An important issue in designing the structures of rubble-mound breakwaters, is to estimate the stability number of its armor block. Most of the traditional stability analysis methods are not compatible enough to handle the obscurities, indistintness and uncertainties of this field. The relations between stability number, damage level and other stability variables can better be modeled using the advanced techniques of machine learning (ML) algorithms. In this prospect, three new ML models consisting of two ensemble learning models; Random Forest, Gradient Boosting and one fully connected deep artificial neural network based prediction model have been presented in this study. Using the ensemble learning models a detailed feature analysis has been introduced here, to understand the feature importances of stability variables on the stability number. To the best of the author's knowledge, these have never been used in this field of stability analysis of rubble-mound breakwaters. Outperforming all of the conventional methods, the proposed study has delivered the highest level of accuracy as 99%, in the prediction of the stability number. Also, the proposed ML models are found to perform better, in dealing with the complex non-linearities related to this field. The feature analysis gives a meaningful insight into the dataset. Therefore, this study can be a useful alternative approach for the designers of the rubble-mound breakwaters.

KW - Breakwater

KW - Ensemble learning

KW - Feature analysis

KW - Machine learning

KW - Neural network

KW - Stability number

UR - http://www.scopus.com/inward/record.url?scp=85133349319&partnerID=8YFLogxK

U2 - 10.1016/j.joes.2022.06.030

DO - 10.1016/j.joes.2022.06.030

M3 - Journal article

AN - SCOPUS:85133349319

JO - Journal of Ocean Engineering and Science

JF - Journal of Ocean Engineering and Science

SN - 2468-0133

ER -