Estimating Pier Scour Depth: Comparison of Empirical Formulations with ANNs, GMDH, MARS, and Kriging
- سال انتشار: 1400
- محل انتشار: مجله هوش مصنوعی و داده کاوی، دوره: 9، شماره: 1
- کد COI اختصاصی: JR_JADM-9-1_011
- زبان مقاله: انگلیسی
- تعداد مشاهده: 307
نویسندگان
Department of Civil Engineering, University of Sistan and Baluchestan, Zahedan, Iran.
Department of Civil Engineering, University of Sistan and Baluchestan, Zahedan, Iran.
Department of Civil Engineering, University of Sistan and Baluchestan, Zahedan, Iran.
Department of Architecture Engineering, University of Sistan and Baluchestan, Zahedan, Iran.
Department of Civil Engineering, Bozorgmehr University of Qaenat, Qaen, Iran.
چکیده
Scouring, occurring when the water flow erodes the bed materials around the bridge pier structure, is a serious safety assessment problem for which there are many equations and models in the literature to estimate the approximate scour depth. This research is aimed to study how surrogate models estimate the scour depth around circular piers and compare the results with those of the empirical formulations. To this end, the pier scour depth was estimated in non-cohesive soils based on a subcritical flow and live bed conditions using the artificial neural networks (ANN), group method of data handling (GMDH), multivariate adaptive regression splines (MARS) and Gaussian process models (Kriging). A database containing ۲۴۶ lab data gathered from various studies was formed and the data were divided into three random parts: ۱) training, ۲) validation and ۳) testing to build the surrogate models. The statistical error criteria such as the coefficient of determination (R۲), root mean squared error (RMSE), mean absolute percentage error (MAPE) and absolute maximum percentage error (MPE) of the surrogate models were then found and compared with those of the popular empirical formulations. Results revealed that the surrogate models’ test data estimations were more accurate than those of the empirical equations; Kriging has had better estimations than other models. In addition, sensitivity analyses of all surrogate models showed that the pier width’s dimensionless expression (b/y) had a greater effect on estimating the normalized scour depth (Ds/y).کلیدواژه ها
Pier scour, Surrogate models, Artificial Neural Network, Kriging, Sensitivity Analysisاطلاعات بیشتر در مورد COI
COI مخفف عبارت CIVILICA Object Identifier به معنی شناسه سیویلیکا برای اسناد است. COI کدی است که مطابق محل انتشار، به مقالات کنفرانسها و ژورنالهای داخل کشور به هنگام نمایه سازی بر روی پایگاه استنادی سیویلیکا اختصاص می یابد.
کد COI به مفهوم کد ملی اسناد نمایه شده در سیویلیکا است و کدی یکتا و ثابت است و به همین دلیل همواره قابلیت استناد و پیگیری دارد.