شبیه‌سازی فشار آب حفره‌‌ای در بدنه ‌سد‌های ‌خاکی‌ حین ساخت با ترکیب شبکه عصبی مصنوعی و الگوریتم‌های فرا ابتکاری

نویسندگان

1 دانشجوی دکترا، گروه مهندسی آب، دانشکده کشاورزی، دانشگاه تبریز، تبریز، ایران

2 2- استاد، گروه مهندسی آب، دانشکده کشاورزی، دانشگاه تبریز، تبریز، ایران

3 استادیار، گروه مهندسی آب، دانشکده کشاورزی، دانشگاه تبریز، تبریز، ایران

4 دانشیار، گروه مهندسی آب، دانشکده کشاورزی، دانشگاه تبریز، تبریز، ایران

چکیده

پیش‌بینی دقیق فشار آب حفره‌ای در بدنه سدهای خاکی حین ساخت، یکی از مهم‌ترین عوامل در مدیریت پایداری سدهای خاکی است. در این تحقیق با استفاده از سه مدل متفاوت شبکۀ عصبی تکاملی شامل شبکۀ عصبی پرسپترون چند لایه با الگوریتم ژنتیک، بهینه‌سازی ازدحام ذرات و الگوریتم رقابت استعماری برای تخمین فشار آب حفره‌ای در بدنه سدهای خاکی کبودوال استان گلستان در زمان ساخت مورد مطالعه و مقایسه قرار گرفته است. پنج ویژگی شامل تراز خاک ریزی، زمان ساخت سد، تراز مخزن (آبگیری)، سرعت آبگیری و سرعت خاک ریزی در طول دوره آماری 1388-1391 یا 4 ساله به عنوان ورودی مدل هیبریدی در پیزومتر EP19.7 انتخاب شدند. ترکیب ورودی‌ها با استفاده از روش انتخاب ویژگی و هیبرید الگوریتم چرخه آب-شبکه عصبی مصنوعی (WCA-ANN) به دست آمده است. با اجرای الگوریتم هیبریدی و آنالیز حساسیت و روش انتخاب ویژگی، تراز خاک ریزی، زمان ساخت سد، تراز آب گیری و سرعت آبگیری به عنوان چهار ورودی برتر انتخاب شدند زیرا ترکیب این 4 ویژگی با مقدارMSE  برابر1587/1 کمترین خطا را دار بوده است. در این مطالعه وزن‌های شبکه عصبی به کمک سه الگوریتم فرا ابتکاری مذکور به منظور افزایش کارایی بهینه‌شده ‌است. در حالت کلی با توجه به شاخص‌های آماری، نتایج حاکی از دقت قابل‌قبول هر سه مدل هیبریدی است. از لحاظ اولویت نیز مدل هیبرید ANN-GA با بیش‌ترین دقت و کمترین خطا و مقادیر ، RMSE و MAE به ترتیب برابر با 9773/0، 0457/0 و 0399/0 در اولویت اول و مدل‌های هیبریدی ANN-PSO و ANN-ICA به ترتیب در اولویت‌های بعدی قرار گرفتند.
 

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Simulation of Pore Water Pressure in the Body of Earth Dams During Construction by Combining Artificial Neural Network and Meta-Heuristic Algorithms

نویسندگان [English]

  • hosein hakimi khansar 1
  • Ali Hosseinzadeh Dalir 2
  • Javad Parsa 3
  • Jalal Shiri 4
1 PhD Student, Department of Water Engineering, Faculty of Agriculture, University of Tabriz, Tabriz, Iran
2 Professore, Department of Water Engineering, University of Tabriz, Iran
3 Associate Professor, University of Tabriz /Department of Water Engineering
4 Assistant Professor, University of Tabriz /Department of Water Engineering
چکیده [English]

Accurate prediction of pore water pressure in the body of earth dams during construction is one of the most important factors in managing the stability of earth dams. In this study, using three different evolutionary neural network models including multilayer perceptron neural network with genetic algorithm, particle swarm optimization and Imperialist Competitive algorithm for estimating the pore water pressure in the body of Kabudwal earth dam at the time of construction, has been studied. Five features including fill level, construction time, reservoir level, impounding rate and fill speed during the 4-year statistical period were selected as the input of the hybrid model in piezometer EP19.7. The composition of the inputs was obtained using the feature selection method and the hybrid water cycle algorithm -artificial neural network. By performing hybrid algorithm and sensitivity analysis and feature selection method, fill level, construction time, reservoir level and dewatering speed were selected as the top four inputs, because the combination of these four features with MSE value of 1.1587 had the least error. In this study, artificial neural network weights are optimized to increase efficiency using the above three meta-heuristic algorithms. In general, according to statistical indicators, the results indicate acceptable accuracy of all three hybrid models. In terms of priority, the ANN-GA hybrid model with the highest accuracy and minimum error and values of , RMSE and MAE are equal to 0.9773, 0.0457 and 0.0399, respectively, is first priority and ANN-PSO and ANN-ICA hybrid models were given the next priorities, respectively.

کلیدواژه‌ها [English]

  • Artificial neural network
  • Earth dam
  • Pore water pressure
  • Genetic algorithm
  • Water cycle algorithm

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دانش آب و خاک
دوره 33، شماره 1
فروردین 1402
صفحه 63-77

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