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تخمین تراوش سدخاکی با استفاده از مدل ترکیبی یادگیری عمیق و تبدیل موجک | ||
| نشریه مهندسی عمران امیرکبیر | ||
| مقاله 15، دوره 56، شماره 11، 1403، صفحه 1387-1406 اصل مقاله (1.55 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22060/ceej.2024.22535.8007 | ||
| نویسندگان | ||
| آیدا حسینی بقانام* ؛ وحید نورانی؛ الناز شرقی؛ زهرا داداش پور | ||
| دانشکده مهندسی عمران، دانشگاه تبریز، تبریز، ایران | ||
| چکیده | ||
| پیشبینی تراوش یکی از ابزارهای مهم در پیشگیری از فرسایش و تخریب سدهای خاکی است. در سالهای اخیر باتوجهبه عدمقطعیت، پیچیدگی و غیرخطیبودن روابط حاکم بر تراوش، استفاده از روشهای هوش مصنوعی برای تخمین و پیشبینی این پدیده موردتوجه قرار گرفته است. هدف از این پژوهش تخمین تراوش سدخاکی ستارخان است. این سد در شمال غربی ایران قرار دارد. برای دستیابی به این هدف، در این پژوهش از شبکه حافظه بلند - کوتاهمدت و مدل ترکیبی موجک - شبکه عمیق در دو سناریو متفاوت استفاده شده است و نتایج بهدستآمده از این مدلها با شبکه عصبی پیشرو مقایسه شده است. نتایج بهدستآمده نشان داد که شبکههای بازگشتی عمیق در مدلسازی پدیده تراوش، به دلیل ارتباط بازگشتی خروجی با ورودی در هر گام زمانی و توانایی یادگیری وابستگیها در دنبالهای از زمانهای قبل، نسبت به شبکههای عصبی پیشرو دقت تخمین بالاتری دارند بهطوری که باعث افزایش دقت مدلسازی تا 7% شده است. هم چنین مدل ترکیبی موجک - شبکه عمیق نسبت به سایر مدلها عملکرد بهتری دارد بهطوری که دقت مدلسازی را تا 10% افزایش داده است. درنهایت میتوان نتیجه گرفت که استفاده از شبکههای بازگشتی عمیق و مدل ترکیبی موجک - شبکه عمیق در مدلسازی تراوش، توانایی بهبود دقت تخمین در پیشبینی این پدیده را دارند. | ||
| کلیدواژهها | ||
| تراوش سد خاکی؛ شبکه عصبی مصنوعی؛ شبکه حافظه بلند - کوتاهمدت (LSTM)؛ تبدیل موجک؛ سد خاکی ستارخان | ||
| موضوعات | ||
| سدهای خاکی؛ نشت | ||
| عنوان مقاله [English] | ||
| Estimation of seepage in earth fill dams using deep learning and wavelet transform | ||
| نویسندگان [English] | ||
| Aida hosseinibaghanam؛ Vahid Nourani؛ Elnaz Sharghi؛ Zahra Dadashpour | ||
| Faculty of Civil Engineering, Tabriz University, Tabriz, Iran. | ||
| چکیده [English] | ||
| Seepage prediction is one of the important tools in preventing erosion and destruction earth-fill dams. In recent years, due to the uncertainty, complexity, and nonlinearity of seepage relationships, the use of artificial intelligence methods for the estimation and prediction of this phenomenon has gained attention. The objective of this research is to estimate seepage in the Sattarkhan earthfill dam located in northwest Iran. To achieve this objective, in this research, the long-short-term memory network and the wavelet-deep network hybrid model have been used in two different scenarios, and the results obtained from these models have been compared with the feed-forward neural network. The results obtained indicated that deep recurrent networks, in the modeling of the seepage phenomenon, outperformed the forward neural networks in terms of estimation accuracy. This can be attributed to their recursive connection between the output and input at each time step, as well as their ability to learn dependencies from previous time sequences. The modeling accuracy was improved by up to 7% as a result. Furthermore, the combined wavelet-deep network model demonstrated superior performance compared to other models, resulting in a 10% increase in modeling accuracy. In conclusion, the utilization of deep recurrent networks and the combined wavelet-deep network model in seepage modeling holds the potential to enhance estimation accuracy when predicting this phenomenon. | ||
| کلیدواژهها [English] | ||
| Earth Fill Dam Seepage, Artificial Neural Network, Long Short-Term Memory (LSTM) Network, Wavelet Transform, Sattarkhan Earth Fill Dam | ||
| مراجع | ||
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