Prediction of Wave Reflection from Berm Breakwaters, Part A: Presenting a New Formula

Document Type : Original Article

Authors

1 School of Civil Engineering, Iran University of Science & Technology, Tehran, Iran

2 Lir National Ocean Test Facility, MaREI, University College Cork, Cork, Ireland

3 Iranian National Institute for Oceanography & Atmospheric Science (INIOAS), Tehran, Iran

4 Faculty of Civil Engineering, Shahrood University of Technology, Shahrood, Iran

10.22044/jhwe.2025.12866.1009

Abstract

Wave reflection is an important hydrodynamic parameter in the design procedure of berm breakwaters. The existing formulae for predicting the wave reflection from berm breakwater are mainly based on regression model of available experimental data. Recently, applications of soft computing approaches and data mining techniques in tackling coastal engineering related problems received considerable attention. In this paper, accuracy of existing berm reflection formulae was evaluated by use of statistical measures and M5′ model tree algorithm is employed to predict the wave reflection with high precision. M5′ model trees are trained and tested with the available experimental data. Both hydrodynamic and structural factors including wave steepness, berm permeability and structure slope have been considered in developing the prediction models. Performance of developed models is tested against the experimental data by using statistical error parameters. The results show that the proposed formulae by M5′ model tree algorithm yields in more accurate prediction of wave reflection from berm breakwaters than existing formulae.

Keywords


Alikhani, A., 2000. On reshaping breakwaters. Hydraulics & Coastal Engineering Laboratory, Aalborg University.
Allsop, W., Channell, A., 1989. Wave reflections in harbours: reflection performance of rock armoured slopes in random waves.
Barros, R.C., Basgalupp, M.P., Ruiz, D.D., de Carvalho, A.C., Freitas, A.A., 2010. Evolutionary model tree induction, Proceedings of the 2010 ACM Symposium on Applied Computing. ACM, pp. 1131-1137.
Batmanghelichi, I., Vafai, F., 2010. Evaluation of Wave Reflection Coefficient from Dynamically Stable Reshaping Berm Breakwaters, Ports 2010. 12th Triannual International Conference. Building on the Past, Respecting the Future.
Bonakdar, L., Etemad-Shahidi, A., 2011. Predicting wave run-up on rubble-mound structures using M5 model tree. Ocean Engineering, 38(1): 111-118.
Burcharth, H.F. et al., 2003. State-of-the-Art of Designing and Constructing Berm Breakwaters. PIANC General Secretariat.
Etemad-Shahidi, A., Bonakdar, L., 2009. Design of rubble-mound breakwaters using M5′ machine learning method. Applied Ocean Research, 31(3): 197-201.
Etemad-Shahidi, A., Yasa, R., Kazeminezhad, M., 2011. Prediction of wave-induced scour depth under submarine pipelines using machine learning approach. Applied Ocean Research, 33(1): 54-59.
Kambekar, A., Deo, M., 2003. Estimation of pile group scour using neural networks. Applied Ocean Research, 25(4): 225-234.
Kazeminezhad, M., Etemad-Shahidi, A., 2010. An alternative approach for investigation of the wave-induced scour around pipelines. Journal of Hydroinformatics, 12(1): 51-65.
Kik, R., 2011. The notional permeability of breakwaters: experimental research on the permeability factor P, TU Delft, Delft University of Technology.
Lykke Andersen, T., 2006. Hydraulic Response of Rubble Mound Breakwaters: scale effects-berm breakwaters, Aalborg University.
Mahjoobi, J., Etemad-Shahidi, A., Kazeminezhad, M., 2008. Hindcasting of wave parameters using different soft computing methods. Applied Ocean Research, 30(1): 28-36.
Postma, G., 1989. Wave reflection from rock slopes under random wave attack, TU Delft, Delft University of Technology.
Quinlan, J.R., 1992. Learning with continuous classes, 5th Australian joint conference on artificial intelligence. Singapore, pp. 343-348.
Shirian, N., Shafieefar, M., Aghtouman, P., Chegini, V., Experimental Investigations and Development of New Relations to Determine Run-down Levels of Irregular Waves on Reshaping Breakwaters.
Sigurdarson, S., Van der Meer, J., 2013. Design of berm breakwaters: Recession, overtopping and reflection. Proceedings of Coasts, Marine Structures and Breakwaters: 18-20.
Suh, K.D., Choi, J.C., Kim, B.H., Park, W.S., Lee, K.S., 2001. Reflection of irregular waves from perforated-wall caisson breakwaters. Coastal Engineering, 44(2): 141-151.
Sulisz, W., 1985. Wave reflection and transmission at permeable breakwaters of arbitrary cross-section. Coastal Engineering, 9(4): 371-386.
Van Der Meer, J.W., 1988. Rock slopes and gravel beaches under wave attack, TU Delft, Delft University of Technology.
Van der Meer, J.W., 1992. Conceptual design of rubble mound breakwaters, ICCE 1992 local organising committee.
Wang, Y., Witten, I.H., 1996. Induction of model trees for predicting continuous classes.
Witten, I.H., Frank, E., 2005. Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann.
Zanuttigh, B., Formentin, S.M., Briganti, R., 2013. A neural network for the prediction of wave reflection from coastal and harbor structures. Coastal Engineering, 80: 49-67.
Zanuttigh, B., Formentin, S.M., Van der Meer, J.W., 2014. ADVANCES IN MODELLING WAVE-STRUCTURE INTERACTION THROUGH ARTIFICIAL NEURAL NETWORKS. Coastal Engineering Proceedings, 1(34): structures. 69.
Zanuttigh, B., van der Meer, J.W., 2006. Wave reflection from coastal structures, Coastal Engineering Conference. ASCE AMERICAN SOCIETY OF CIVIL ENGINEERS, pp. 4337.
Zanuttigh, B., van der Meer, J.W., Lykke Andersen, T., Lara, J.L., Inigo, J.L., 2008. Analysis of wave reflection from structures with berms through an extensive database and 2DV numerical modeling. Proc. Coastal Eng, 2008: 3285-3297.