کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
5473388 | 1520333 | 2017 | 12 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Prediction of non-breaking wave induced scour depth at the trunk section of breakwaters using Genetic Programming and Artificial Neural Networks
ترجمه فارسی عنوان
پیش بینی عدم عمق آبشستگی ناشی از موج شکستگی در بخش تنه موج شکن با استفاده از برنامه های ژنتیک و شبکه های عصبی مصنوعی
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
سایر رشته های مهندسی
مهندسی دریا (اقیانوس)
چکیده انگلیسی
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 (Smax/Hnb). The models have been built using the relative water depth at the toe (htoe/Lnb), the Shields parameter (θ), the non-breaking wave steepness (Hnb/Lnb), and the reflection coefficient (Cr), 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.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Coastal Engineering - Volume 121, March 2017, Pages 107-118
Journal: Coastal Engineering - Volume 121, March 2017, Pages 107-118
نویسندگان
Ali Pourzangbar, Miguel A. Losada, Aniseh Saber, Lida Rasoul Ahari, Philippe Larroudé, Mostafa Vaezi, Maurizio Brocchini,