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Neural Computing and Applications

, Volume 29, Issue 10, pp 705–719 | Cite as

Estimation of nearshore wave transmission for submerged breakwaters using a data-driven predictive model

  • Amir Sharif Ahmadian
  • Richard R. Simons
Original Article

Abstract

The functional design of submerged breakwaters is still developing, particularly with respect to modelling of the nearshore wave field behind the structure. This paper describes a method for predicting the wave transmission coefficients behind submerged breakwaters using machine learning algorithms. An artificial neural network using the radial-basis function approach has been designed and trained using laboratory experimental data expressed in terms of non-dimensional parameters. A wave transmission coefficient calculator is presented, based on the proposed radial-basis function model. Predictions obtained by the radial-basis function model were verified by experimental measurements for a two dimensional breakwater. Comparisons reveal good agreement with the experimental results and encouraging performance from the proposed model. Applying the proposed neural network model for predictions, guidance is given to appropriately calculate wave transmission coefficient behind two dimensional submerged breakwaters. It is concluded that the proposed predictive model offers potential as a design tool to predict wave transmission coefficients behind submerged breakwaters. A step-by-step procedure for practical applications is outlined in a user-friendly form with the intention of providing a simplified tool for preliminary design purposes. Results demonstrate the model’s potential to be extended to three dimensional, rough, permeable structures.

Keywords

Submerged breakwater Nearshore wave transmission Numerical modeling Artificial neural network Radial-basis function Predictive model 

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Copyright information

© The Natural Computing Applications Forum 2016

Authors and Affiliations

  1. 1.Department of Civil Engineering, Khajeh Nasir Building, Faculty of EngineeringUniversity of HormozganBandar AbbasIran
  2. 2.Department of Civil, Environmental and Geomatic Engineering, Chadwick BuildingUCLLondonUK

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