Development and Performance Evaluation of an Artificial Neural Network Model for Estimating Wave Forces on the Pavement behind a Sloping Seawall

In this study, an artificial neural network (ANN) model was developed based on the experimental data obtained from Ko et al. (2022) and Lee et al. (2022) to predict the wave forces acting on the pavement behind a sloping seawall. The ANN models were constructed by varying the number of nodes in the hidden layer and testing various combinations of input variables, both in dimensional and dimensionless forms. The developed neural network models demonstrated excellent wave force prediction performance. In particular, the models established using two heterogeneous sets of experimental data showed significantly better prediction performance than the empirical formulas suggested by Ko et al. (2022) and Lee et al. (2022), respectively.

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Publication Details

Journal
한국해안·해양공학회논문집
Published
2026-08-27
DOI
https://doi.org/10.9765/kscoe.2026.38.4.172
Primary Topic
Hydrological Forecasting Using AI
Type
article
Field-Weighted Citation Impact
0.00

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article

Development and Performance Evaluation of an Artificial Neural Network Model for Estimating Wave Forces on the Pavement behind a Sloping Seawall

Sang-Ho Oh, Sung-Joon Park, Tae-Wan Kim
한국해안·해양공학회논문집
Hydrological Forecasting Using AI
article

Development and Performance Evaluation of an Artificial Neural Network Model for Estimating Wave Forces on the Pavement behind a Sloping Seawall

Sang-Ho Oh, Sung-Joon Park, Tae-Wan Kim
article en

Abstract

In this study, an artificial neural network (ANN) model was developed based on the experimental data obtained from Ko et al. (2022) and Lee et al. (2022) to predict the wave forces acting on the pavement behind a sloping seawall. The ANN models were constructed by varying the number of nodes in the hidden layer and testing various combinations of input variables, both in dimensional and dimensionless forms. The developed neural network models demonstrated excellent wave force prediction performance. In particular, the models established using two heterogeneous sets of experimental data showed significantly better prediction performance than the empirical formulas suggested by Ko et al. (2022) and Lee et al. (2022), respectively.

한국해안·해양공학회논문집Vol. 38(4)
Changwon National University (KR)
Ministry of Oceans and Fisheries
Life below water
Openalex Percentile: Top 17%
Hydrological Forecasting Using AI
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