Ocean Systems Engineering

  • 제7권2호
  • /
  • Pages.75-87
  • /
  • 2017
  • /
  • 2093-6702(pISSN)
  • /
  • 2093-677X(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Current approaches of artificial intelligence in breakwaters - A review

  • 투고 : 2017.01.25
  • 심사 : 2017.05.08
  • 발행 : 2017.06.25
⟨ 이전 논문 다음 논문 ⟩

초록

A breakwater has always been an ideal option to prevent shoreline erosion due to wave action as well as to maintain the tranquility in the lagoon area. The effects of the impinging wave on the structure could be analyzed and evaluated by several physical and numerical methods. An alternate approach to the numerical methods in the prediction of performance of a breakwater is Artificial Intelligence (AI) tools. In the recent decade many researchers have implemented several Artificial Intelligence (AI) tools in the prediction of performance, stability number and scour of breakwaters. This paper is a comprehensive review which serves as a guide to the current state of the art knowledge in application of soft computing techniques in breakwaters. This study aims to provide a detailed review of different soft computing techniques used in the prediction of performance of different breakwaters considering various combinations of input and response variables.

키워드

참고문헌

  1. Azamathulla, H., Asce, M. and Ghani, A.A. (2011), "ANFIS-based approach for predicting thescour depth at culvert outlets", 2(February), 35-40.
  2. Balas, C.E., Koc, M.L. and Tur, R. (2010), "Artificial neural networks based on principal component analysis, fuzzy systems and fuzzy neural networks for preliminary design of rubble mound breakwaters", Appl. Ocean Res., 32(4), 425-433. https://doi.org/10.1016/j.apor.2010.09.005
  3. Castro, A., Pinto, F.T. and Iglesias, G. (2016), "Artificial intelligence applied to plane wave reflection at submerged breakwaters", 1686(December).
  4. Erdik, T. (2009), "Fuzzy logic approach to conventionalrubble mound structures design", Exp. Syst. Appl., 36(3), 4162-4170. https://doi.org/10.1016/j.eswa.2008.06.012
  5. Erdik, T., Savci, M.E. and Sen, Z. (2009), "Artificial neural networks for predicting maximum wave runup on rubble mound structures", Exp. Syst. Appl., 36(3), 6403-6408. https://doi.org/10.1016/j.eswa.2008.07.049
  6. Etemad-Shahidi, A. and Bonakdar, L. (2009), "Design of rubble-mound breakwaters using M5' machine learning method", Appl. Ocean Res., 31(3), 197-201. https://doi.org/10.1016/j.apor.2009.08.003
  7. Goyal, R., Singh, K. and Hegde, A.V. (2014), "Quarter circular breakwater : Prediction and artificial neural network", Mar. Technol. Soc. J., 48, 1-7.
  8. Goyal, R., Singh, K., Hegde, A.V. and Thakur, G.S. (2015). "Prediction of hydrodynamic characteristics of quarter circular breakwater using stepwise regression", Int. J.Ocean Clim. Syst., 6(1), 47-54. https://doi.org/10.1260/1759-3131.6.1.47
  9. Harish, N., Mandal, S., Rao, S.and Patil, S.G. (2015). "Particle swarm optimization based support vector machine for damage level prediction of non-reshaped berm breakwater", Appl. Soft Comput., 27, 313-321. https://doi.org/10.1016/j.asoc.2014.10.041
  10. Jabbari, E.and Talebi, O. (2011). "Using artificial neural networks for estimation of scour at the head of vertical wall breakwater", J. Coast. Res., 64, 521-526.
  11. Jain, P. and Deo, M.C. (2008), "Artificial neural networks for coastal and ocean studies", Proceedings of the12th Int. Conf. Int. Assoc. Comput. Methods Adv. Geomech.
  12. Janardhan, P., Harish, N., Rao, S. and Shirlal, K.G. (2015), "Performance of variable selection method for the damage level prediction of reshaped berm breakwater", Aquat. Procedia, 4, 302-307. https://doi.org/10.1016/j.aqpro.2015.02.041
  13. Kim, D.H., Kim, Y.J. and Hur, D.S. (2014), "Artificial neural network based breakwater damage estimation considering tidal level variation", Ocean Eng., 87, 185-190. https://doi.org/10.1016/j.oceaneng.2014.06.001
  14. Koc, M.L. and Balas,C.E. (2012), "Genetic algorithms based logic-driven fuzzy neural networks for stability assessment of rubble-mound breakwaters", Appl. Ocean Res., 37, 211-219. https://doi.org/10.1016/j.apor.2012.04.005
  15. Koc, M.L., Balas, C.E. and Koc, D.İI. (2016), "Stability assessment of rubble-mound breakwaters using genetic programming", Ocean Eng., 111, 8-12. https://doi.org/10.1016/j.oceaneng.2015.10.058
  16. Lee, A., Kim, S.E. and Suh, K.D. (2015), "Estimation of stability number of rock armor using artificial neural network combined with principal component analysis", Procedia Eng., 116(1), 149-154. https://doi.org/10.1016/j.proeng.2015.08.276
  17. Nikoo, M.R., Varjavand, I., Kerachian, R., Pirooz, M.D. and Karimi, A. (2014), "Multi-objective optimum design of double-layer perforated-wall breakwaters: Application of NSGA-II and bargaining models", Appl. Ocean Res., 47, 47-52. https://doi.org/10.1016/j.apor.2013.12.001
  18. N, S.R.and Deka, P.C. (2014), "Support vector machine applications in the field of hydrology : A review", Appl. Soft Comput. J., 19, 372-386. https://doi.org/10.1016/j.asoc.2014.02.002
  19. Patil, S.G., Mandal, S. and Hegde, A.V. (2012), "Genetic algorithm based support vector machine regression in predicting wave transmission of horizontally interlaced multi-layer moored floating pipe breakwater", Adv. Eng. Softw., 45(1), 203-212. https://doi.org/10.1016/j.advengsoft.2011.09.026
  20. Patil, S.G., Mandal, S., Hegde, A.V. and Alavandar, S. (2011), "Neuro-fuzzy based approach for wave transmission prediction of horizontally interlaced multi-layer moored floating pipe breakwater", Ocean Eng., 38(1), 186-196. https://doi.org/10.1016/j.oceaneng.2010.10.009
  21. Raju, B., Hegde, A.V. and Chandrashekar, O. (2015), "Computational intelligence on hydrodynamic performance characteristics of emerged perforated quarter circle breakwater", Procedia Eng., 116(1), 118-124. https://doi.org/10.1016/j.proeng.2015.08.272
  22. Yagci, O., Mercan, D.E., Cigizoglu, H.K. and Kabdasli, M.S. (2005), "Artificial intelligence methods in breakwater damage ratio estimation", Ocean Eng., 32(17-18), 2088-2106. https://doi.org/10.1016/j.oceaneng.2005.03.004
  23. Zanuttigh, B., Mizar, S. and Briganti, R. (2013), "A neural network for the prediction of wave reflection form coastal and harbor structures", Coast. Eng., 80, 49-67. https://doi.org/10.1016/j.coastaleng.2013.05.004

피인용 문헌

  1. A simplified algorithm for conceptual estimation of the material quantities of rubble-mound breakwaters vol.10, pp.1, 2020, https://doi.org/10.12989/ose.2020.10.1.111
  2. Estimation of the wind speed in Sivas province by using the artificial neural networks vol.32, pp.2, 2017, https://doi.org/10.12989/was.2021.32.2.161
  3. Assessment of global wave forces and moments on porous vertical barriers in random wave fields vol.11, pp.3, 2017, https://doi.org/10.12989/ose.2021.11.3.237