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To maintain the calmness of the harbor, rubble mound breakwaters are one of the practical measures to protect sailing and mooring waters. This paper investigates the effect of relocated rubble mound breakwaters on tranquillity in Busan yacht range and the impact of bridge piers at the entrance of the yacht range based on physical hydraulic modeling...
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... we created a harmonic signal from the wave spectrum and the directionally dispersed functions of wave. As shown in Fig. 5 , there are three types a random wave signal (A), (B), (C). We used random wave signal (C)-type in these experiments. We decided to use it to create the (c)-type of random wave that can realize storm surge. In this experiment, it is assumed to adopt S max = 75 for the design wave and the normal wave is determined not to be a larger ...
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... a big role in entrance of marina. Fig. 14 represented two cases C3-1 (a) and C3-2 (b) with the elevation of west breakwater to + 7.50 m with NDB, Design Wave;SSE, H i = 4.6 m, T 0 = 15.16 s, HWL, HWL + storm surge without bridge pier. The result helped to reduce wave overtopping, run-up elevation, and significant wave height in the west side. Fig. 15 revealed the Origin Design Breakwater and New Design Breakwater with similar direct waves and having the different influence of reflection and diffraction impact in the entrance of ...
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Citations
... Breakwaters are one of the most significant structures for the protection of coastal areas [41]. It protects the coast from the waves coming offshore and prevents the siltation in river mouths to maintain the balance. ...
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 importances of the stability variables on 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 with a coefficient determination of 0.9943 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 physical insight into the dataset. Therefore, this study can be a useful alternative approach for the designers of the rubble-mound breakwaters.
... Patil et al. [20] incorporated SVM and Genetic Algorithms (GAs) to predict the wave transmission through floating breakwaters based on experimental wave transmission data from regular wave flumes at marine structure laboratory. Furthermore, many hybrid models are utilized for modelling sedimentation issues and for many other applications [21][22][23][24][25][26][27][28][29][30][31] . However, to the best of the authors' knowledge, no AI approaches were used to study the sedimentation and to quantify the relationship between the whole physical parameters of harbors' basins and the accumulation of sedimentation. ...
Sedimentation is one of the most critical environmental issues facing harbors’ authorities that results in significant maintenance and dredging costs. Thus, it is essential to plan and manage the harbors in harmony with both the environmental and economic aspects to support Integrated Coastal Structures Management (ICSM). Harbors' layout and the permeability of protection structures like breakwaters affect the sediment transport within harbors’ basins. Using a multi-step relational research framework, this study aims to design a novel prediction model for estimating the sedimentation quantities in harbors through a comparative approach based on artificial intelligence (AI) algorithms. First, one hundred simulations for different harbor layouts and various breakwater characteristics were numerically performed using a coastal modeling system (CMS) for generating the dataset to train and validate the proposed AI-based models. Second, three AI approaches namely: Support Vector Regression (SVR), Gaussian Process Regression (GPR), and Artificial Neural Networks (ANN) were developed to predict sedimentation quantities. Third, a comparison between the developed models was conducted using quality assessment criteria to evaluate their performance and choose the best one. Fourth, a sensitivity analysis was performed to provide insights into the factors affecting sedimentation. Lastly, a decision support tool was developed to predict harbors' sedimentation quantities. Results showed that the ANN model outperforms other models with mean absolute percentage error (MAPE) equals 4%. Furthermore, sensitivity analysis demonstrated that the main breakwater inclination angle, porosity, and harbor basin width affect significantly sediment transport. This research makes a significant contribution to the management of coastal structures by developing an AI data-driven framework that is beneficial for harbors' authorities. Ultimately, the developed decision-support AI tool could be used to predict harbors' sedimentation quantities in an easy, cheap, accurate, and practical manner compared to physical modeling which is time-consuming and costly.
