Prediction of non-breaking wave induced scour depth at the trunk section of breakwaters using Genetic Programming and Artificial Neural Networks

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Article{Pourzangbar:2017:CE,
  author =       "Ali Pourzangbar and Miguel A. Losada and 
                 Aniseh Saber and Lida Rasoul Ahari and Philippe Larroude and 
                 Mostafa Vaezi and Maurizio Brocchini",
  title =        "Prediction of non-breaking wave induced scour depth at
                 the trunk section of breakwaters using Genetic
                 Programming and Artificial Neural Networks",
  journal =      "Coastal Engineering",
  volume =       "121",
  pages =        "107--118",
  year =         "2017",
  ISSN =         "0378-3839",
  DOI =          "doi:10.1016/j.coastaleng.2016.12.008",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0378383916304586",
  abstract =     "Scour may act as a threat to coastal structures
                 stability and reduce their functionality. Thus,
                 protection against scour can guarantee these
                 structures' intended performance, which can be achieved
                 by the accurate prediction of the maximum scour depth.
                 Since the hydrodynamics of scour is very complex,
                 existing formulas cannot produce good predictions.
                 Therefore, in this paper, Genetic Programming (GP) and
                 Artificial Neural Networks (ANNs) have been used to
                 predict the maximum scour depth at breakwaters due to
                 non-breaking waves ( S max / H n b ). The models have
                 been built using the relative water depth at the toe (
                 h t o e / L n b ), the Shields parameter ( θ ), the
                 non-breaking wave steepness ( H n b / L n b ), and the
                 reflection coefficient ( C r ), where in the case of
                 irregular waves, Hnb=Hrms, Tnb=Tpeak and Lnb is the
                 wavelength associated with the peak period (Lnb=Lp). 95
                 experimental datasets gathered from published
                 literature on small-scale experiments have been used to
                 develop the GP and ANNs models. The results indicate
                 that the developed models perform significantly better
                 than the empirical formulas derived from the mentioned
                 experiments. The GP model is to be preferred, because
                 it performed marginally better than the ANNs model and
                 also produced an accurate and physically-sound equation
                 for the prediction of the maximum scour depth.
                 Furthermore, the average percentage change (APC) of
                 input parameters in the GP and ANNs models shows that
                 the maximum scour depth dependence on the reflection
                 coefficient is larger than that of other input
                 parameters.",
  keywords =     "genetic algorithms, genetic programming, Scour,
                 Non-breaking waves, Artificial Neural Networks (ANNs),
                 Breakwater, Uncertainty assessment",
}

Genetic Programming entries for Ali Pourzangbar Miguel A Losada Aniseh Saber Lida Rasoul Ahari Philippe Larroude Mostafa Vaezi Maurizio Brocchini

Citations