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کارایی مدل هیبریدی NNA-ANN در مدل‌سازی تنش خاک در بدنه سد‌های خاکی در زمان ساخت و مقایسه با مدل عددی (مطالعه موردی سدکبودوال) | ||
| تحقیقات مهندسی سازه های آبیاری و زهکشی | ||
| دوره 22، شماره 85، فروردین 1401، صفحه 115-136 اصل مقاله (1.27 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22092/idser.2022.356196.1490 | ||
| نویسندگان | ||
| حسین حکیمی خانسر* 1؛ جواد پارسا2؛ علی حسین زاده دلیر3؛ جلال شیری4 | ||
| 1دانشجوی دکترا، گروه مهندسی آب، دانشکده کشاورزی، دانشگاه تبریز، تبریز، ایران | ||
| 2استادیار، گروه مهندسی آب، دانشکده کشاورزی، دانشگاه تبریز، تبریز، ایران | ||
| 3استادگروه مهندسی آب، دانشکده کشاورزی، دانشگاه تبریز، تبریز، ایران | ||
| 4دانشیار، گروه مهندسی آب، دانشکده کشاورزی، دانشگاه تبریز، تبریز، ایران | ||
| چکیده | ||
| تخمین دقیق مقادیر تنش خاک در بدنه سد خاکی در زمان ساخت از اقدامات ضروری برای مدیریت پایداری آن است. در این پژوهش، تأثیرگذارترین ویژگی‌ها در مدل‌سازی تنش خاک به صورت مطالعه موردی (سدکبودوال) با استفاده از الگوریتم هیبرید شبکه عصبی- شبکه عصبی مصنوعی(NNA-ANN) تعیین شد و مقایسه‌ای بین نتایج مدل هیبریدی با مدل عددی صورت پذیرفت. پنج ویژگی شامل تراز مخزن، تراز خاکریزی، زمان ساخت، سرعت آبگیری و سرعت خاکریزی برای ورودی مدل هیبریدی هوشمند انتخاب گردید. با استفاده از الگوریتم هیبریدی و روش انتخاب ویژگی، ترکیب سه ویژگی، شامل تراز خاکریزی، زمان ساخت سد و تراز آب گیری (با RMSE برابر با 5024/0) مؤثرترین ویژگی‌ها در مدل‌سازی تنش کل در سلول منتخب بودند. نتایج نشان داد که مدل هیبریدی در سد کبودوال با مقادیرR^2، RMSE، MAE وNS به ترتیب برابر با 9943/0، 5653/2، 9973/1 و 9999/0 دارای عملکرد بهتری در مدل‌سازی تنش کل خاک نسبت به مدل عددی با مقادیرR^2، RMSE، MAE وNS به ترتیب برابر با 9625/0، 2567/26، 1667/25 و 9772/0 است. این پیش‌بینی برای سایر سلول‌ها در مقاطع مختلف سد مذکور، نیز قابل استناد است. نتایج حاصل از این تحقیق برای ساخت گاه جدید با مشخصات ژئوتکنیکی جدید یعنی سد مسجد سلیمان نیز معتبر بود ولی برای هر ساخت گاه از ترکیب مناسب به خود باید استفاده کرد. | ||
| کلیدواژه‌ها | ||
| انتخاب ویژگی؛ سلول تنش سنجی خاک؛ المان محدود؛ هوش مصنوعی | ||
| عنوان مقاله [English] | ||
| Efficiency of NNA-ANN hybrid model in modeling soil stress in the body of earth dams during construction and comparison with numerical model (Case study of Kaboud-val dam) | ||
| نویسندگان [English] | ||
| hosein hakimi khansar1؛ Javad Parsa2؛ Ali Hosseinzadeh Dalir3؛ Jalal Shiri4 | ||
| 1Ph.D Candidate, University of Tabriz / Department of Water Engineerin, University of Tabriz, Iran | ||
| 2Assist. Prof., Department of Water Engineering, University of Tabriz, Iran | ||
| 3Professor, Department of Water Engineering, University of Tabriz, Tabriz, Iran | ||
| 4Associate Professor, Department of Water Engineering, University of Tabriz, Iran | ||
| چکیده [English] | ||
| The stress created in the soil significantly affects its engineering behavior. Changing its value during the construction of earthen dams causes volume change and shear strength, causing rupture, soil compaction and settlement in earthen dams. So measuring soil stresses of dams is essential that it is done by instrumentation installed. Artificial intelligence models such as artificial neural networks for modeling many engineering applications. Also by extending the meta-heuristic algorithms, combined with neural networks have become very popular due to more accurate results. Methodology In this study, the cross-section 19 (cross-section of the middle part of the body and dam foundation) for the modeling of soil stress were used during the construction of the dam Kaboud-val. Also Kaboud-val dam instrumentation data (derived from Golestan Regional Water Co.) was used at the time of construction during the period of 4 years. Type and number of input data is the most important thing in modeling artificial intelligence. By examining data TPC19.1 cells in section 19 in Kaboud-val dam, embankment alignment (F), the water level of the reservoir (R), the construction of the dam (T), speed filling and dewatering speed was selected for input. The soil stress (P) in the body of the dam during construction, intelligent model was selected for output. This process is most effective in a subset of features from the set of input features according to the least error, selected and additional features will be removed. In this research, a meta-algorithm (artificial neural network (NNA) algorithm) is combined with an artificial neural network (ANN) that has the ability to predict complex and nonlinear relationships and extracts effective features for modeling soil stress with appropriate accuracy. In this study, the most effective features in soil stress modeling were determined in a case study (Kaboud-val Dam) using the NNA-ANN hybrid algorithm and a comparison was made between the results of the hybrid model and the numerical model. Five features include reservoir level, fill level, dam construction time, impounding velocity and fill velocity was selected for the input. Results and Discussion Using hybrid algorithm and feature selection method, a combination of three features, including reservoir level, fill level, dam construction time (with RMSE equal to 0.5024) were the most effective features in modeling soil stress in the selected cell. The results showed that the hybrid model in Kaboud-val Dam (with values of R^2, RMSE, MAE and NS equal to 0.9943, 2.5653, 1.9973 and 0.9999, respectively) has better performance in modeling soil stress than the numerical model. (With values of R^2, RMSE, MAE and NS are equal to 0.9625, 26.2567, 16.6725 and 0.9772, respectively). The results showed that the reduction in the input features to reduce the time and cost reduction is more economical and more effective. Because with the increase in the number of features in the hybrid model, the increase in modeling accuracy did not occur. Sensitivity analysis showed that the dam construction time and fill level, of the highest sensitivity factor, the most important feature of the model is the total stress in cells. Modeling with the mentioned features, in another dam with a new construction site and new geotechnical specifications (Masjed Soleiman Dam) showed that the use of artificial intelligence model according to statistical indicators has more accurate answers than the numerical model. Conclusion The results showed that the use of artificial intelligence methods in the design and initial estimates of soil stress parameters in earthen dams instead of using numerical methods has high reliability and accuracy. The combination of input data in the hybrid model under study is suitable for Kaboud-val dam and Masjed Soleiman dam and the appropriate combination should be used for each construction site. By completing the number of data in different sections of the dam and the number of construction sites in areas with similar climate and geotechnical conditions, a design software can be obtained to predict the amount of soil stress during construction in the body and foundations of earth dams. | ||
| کلیدواژه‌ها [English] | ||
| Feature selection, Soil stress cell, Finite element, Neural network algorithm | ||
| مراجع | ||
|
AmiriMijan, F., Shirani, H., Esfandiarpour, I., Besalatpour, A., & Shekofteh, H. (2019). Identifying the Determinant Factors Influencing S Index in Calcereous Soils Using Anneling Simulated– Artificial Neural Network Hybrid Algorithm [Research]. Journal of Water and Soil Science, 23(3), 381-394. https://doi.org/10.47176/jwss.23.3.17551. (in Persian)
Bolouri-Bazaz, J., & Mobini-Zad, M. (2010). Evaluation of the behavior of Nahreyn embankment dam during construction and comparing instrumentation data with the results of a finite element code. Iranian Water Research Journal, 4(6), 1-15. https://www.magiran.com/paper/835543 (in Persian)
Brinkgreve, R., & Vermeer, P. (2001). Plaxis 2D, general information-reference & scientific manual. In: Balkema Publisher. Netherlands.
Chandrashekar, G., & Sahin, F. (2014). A survey on feature selection methods. Computers & Electrical Engineering, 40(1), 16-28.
Cucci, G., Lacolla, G., Pagliai, M., & Vignozzi, N. (2015). Effect of reclamation on the structure of silty-clay soils irrigated with saline-sodic waters. International agrophysics, 29(1).
Hakimi Khansar, H. (2021). Simulation of behavior the Kabudval Dam during construction with 3D numerical modeling. Amirkabir Journal of Civil Engineering, 53(9), 20-20. https://doi.org/10.22060/ceej.2020.18172.6790 (in Persian)
Hakimi Khansar, H., Golmaei, H., & Sheidaeian, M. (2014). Evaluting of Kaboodval dam during construction using finite clement method with software PLAXIS and a comparison with the actual values of instrumentation data. Journal of Water Science & Engineering, 4(9), 33-50. http://wsej.iauahvaz.ac.ir/article_531129_9c5d0549d922be8a56971dc85bc286cc.pdf
Haykin, S. (1998). Neural Networks, A Comprehensive foundation. Prenticer Hall. In: Inc.
Hill, M. C. (2000). Methods and guidelines for effective model calibration. In Building Partnerships (pp. 1-10).
Jeon, Y. S., & Yang, H. S. (2004). Development Of A Back Analysis Algorithm Using Flac. International Journal of Rock Mechanics and Mining Sciences, 41, 447-453. https://doi.org/https://doi.org/10.1016/j.ijrmms.2004.03.081
Karakus, M., & Fowell, R. (2005). Back analysis for tunnelling induced ground movements and stress redistribution. Tunnelling and Underground Space Technology, 20(6), 514-524.
Komasi, M., & Beiranvand, b. (2020). Study of Vertical and Horizontal Displacements of Eyvashan Earth Dam Using Instrumentation and Numerical Analysis. Iranian Journal of Soil and Water Research, 51(1), 245-256. https://doi.org/10.22059/ijswr.2019.281594.668205
Kumar, V., & Minz, S. (2014). Feature selection: a literature review. SmartCR, 4(3), 211-229.
Lippmann, R. (1987). An introduction to computing with neural nets. IEEE Assp magazine, 4(2), 4-22.
Norouzi, R., Sihag, P., Daneshfaraz, R., Abraham, J., & Hasannia, V. (2021). Predicting relative energy dissipation for vertical drops equipped with a horizontal screen using soft computing techniques. Water Supply.
Nourani, V. (2015). Basics of hydroinformatics. Farsi. Tabriz University Press, Tabriz.
Nourani, V., & Babakhani, A. (2013). Integration of Artificial Neural Networks with Radial Basis Function Interpolation in Earthfill Dam Seepage Modeling. Journal of Computing in Civil Engineering, 27(2), 183-195. https://doi.org/doi:10.1061/(ASCE)CP.1943-5487.0000200
Nourani, V., Sharghi, E., & Aminfar, M. (2012). Integrated ANN model for earthfill dams seepage analysis: Sattarkhan Dam in Iran. Artif. Intell. Res., 1(2), 22-37. https://doi.org/10.5430/air.v1n2p22
Nouri, M., & Salmasi, F. (2019). Predicting Seepage of Earth Dams using Artificial Intelligence Techniques. Irrigation Sciences and Engineering, 42(1), 83-97. https://doi.org/10.22055/jise.2017.21384.1537
Novakovic, A., Rankovic, V., Grujovic, N., Divac, D., & Milivojevic, N. (2014). Development of neuro-fuzzy model for dam seepage analysis. Annals of the Faculty of Engineering Hunedoara, 12(2), 133-136.
Parsa, J., Hakimi Khansar, H., Hoseinzadeh dalir, A., & Shiri, J. (2021). Simulation of soil stress in earth dams using artificial intelligence models and determination of effective features. Amirkabir Journal of Civil Engineering, -. https://doi.org/10.22060/ceej.2021.18682.6925 (in Persian)
Ranković, V., Grujović, N., Divac, D., & Milivojević, N. (2014). Development of support vector regression identification model for prediction of dam structural behaviour. Structural Safety, 48, 33-39. https://doi.org/https://doi.org/10.1016/j.strusafe.2014.02.004
Rashidi, M., & Haeri, S. M. (2017). Evaluation of behaviors of earth and rockfill dams during construction and initial impounding using instrumentation data and numerical modeling. Journal of Rock Mechanics and Geotechnical Engineering, 9(4), 709-725.
Sadollah, A., Sayyaadi, H., & Yadav, A. (2018). A dynamic metaheuristic optimization model inspired by biological nervous systems: Neural network algorithm. Applied Soft Computing, 71, 747-782.
Salmasi, F., Hakimi Khansar, H., & Norani, B. (2019). Investigation of the Structure of the Dam Body during Construction and its Comparison with the Analytical Results Using PLAXIS Software (the Case Study of Kaboodvall Dam) [Research]. Journal of Water and Soil Science, 22(4), 155-171. https://doi.org/10.29252/jstnar.22.4.155 (in Persian)
Tayfur, G., Swiatek, D., Wita, A., & Singh, V. P. (2005). Case study: Finite element method and artificial neural network models for flow through Jeziorsko earthfill dam in Poland. Journal of Hydraulic Engineering, 131(6), 431-440.
Wu, K., Soci, C., Shum, P. P., & Zheludev, N. I. (2014). Computing matrix inversion with optical networks. Optics express, 22(1), 295-304. | ||
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