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مدلسازی ضریب دبی سرریزهای اوجی قوس محور با تقرب دیوارههای جانبی در شرایط جریان آزاد با استفاده ازروشهای یادگیری ماشینی | ||
| تحقیقات مهندسی سازه های آبیاری و زهکشی | ||
| مقاله 9، دوره 18، شماره 69، آبان 1396، صفحه 107-122 اصل مقاله (1.08 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22092/aridse.2017.109506.1191 | ||
| نویسندگان | ||
| کیومرث روشنگر* 1؛ علی فرودی خور2؛ مجتبی صانعی3 | ||
| 1دانشیار مهندسی عمران دانشگاه تبریز | ||
| 2دانشجوی دکتری عمران- دانشگاه تبریز | ||
| 3عضو هیئت علمی و معاون پژوهشی پژوهشکده حفاظت خاک و آبخیزداری | ||
| چکیده | ||
| سرریزهای قوسی به دلیل شرایط هیدرولیکی خاص و طول تاج بلند، قابلیت مناسبی در تنظیم مؤثر تراز سطح آب در سدها و شبکههای آبیاری و زهکشی دارند. اما تاکنون در زمینه تخمین ضریب دبی این سرریزها و نیز استخراج رابطه دبی-اشل آنها تحقیقات بسیار کمی انجام شده است. لذا دراین تحقیق از روشهای برنامهریزی بیان ژن(GEP) و روش شبکه عصبی مصنوعی (ANN) ضریب دبی سرریزهای اوجی قوس محور با دیوارههای هادی همگرا مدلسازی و با داده های آزمایشگاهی مورد مقایسه قرار گرفت. بدین منظور از داده های مدل فیزیکی سرریز سد گرمیچای با چندین زاویه همگرایی دیوارههای هادی 0 ̊ | ||
| کلیدواژهها | ||
| سرریز اوجی؛ دیوارههای هادی؛ ضریب دبی؛ برنامه ریزی ژنتیک(GEP)؛ شبکه عصبی مصنوعی | ||
| عنوان مقاله [English] | ||
| Modeling the discharge coefficient of converging ogee spillways for free flow conditions using machine learning approaches | ||
| نویسندگان [English] | ||
| qiumars roushangar1؛ ALi Forudi2؛ mojtaba saneie3 | ||
| 3Assistant Professer, Soil Conservation and Watershed Management Institute (SCWMRI),Tehran,Iran . Postal addresses: | ||
| چکیده [English] | ||
| Ogee crested spillways having superb hydraulic properties including simplicity in design and flow passing efficacy. So far, limited research in the area of prediction and the extraction of discharge coefficient relationship is conducted. In current study two different methods for modeling the discharge coefficient of the converging ogee spillway with a curve axis by was developed and results were compared with the observed experimental values through the Genetic Expression Programming (GEP) and artificial networks (ANNs) approaches. For this purpose, the experimental data of the Germi chay ogee spillway model with varying training wall convergence angles (), was used. Based on the obtained results, applied Artificial Intelligence (AI) models have reliable performance in predicting the discharge coefficient of converging ogee spillways. Moreover, the performance of GEP model is a bit better than ANN technique with relatively low error and high correlation values. To recognize the most effective variables on the discharge coefficient, sensitivity analysis of GEP for the best model was carried out. Results showed that ratio of the design head to the critical depth (Hd/yc) and ratio of the crest length to the downstream channel width (L/Lch) are the most and least important parameters in predicting the discharge coefficient of the converging ogee spillway respectively. The best evaluation of test series were observed in GEP approach with the values of DC=0.818 and RMSE=0.089 and in ANNs approach with the values of DC=0.77 and RMSE=0.099 which demonstrates the high accuracy of predictions. | ||
| کلیدواژهها [English] | ||
| ANN, Discharge Coefficient, GEP, ogee spillway, training walls | ||
| مراجع | ||
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