| تعداد نشریات | 282 |
| تعداد نشریات سازمان | 80 |
| تعداد شمارهها | 3,196 |
| تعداد مقالات | 35,423 |
| تعداد مشاهده مقاله | 52,242,265 |
| تعداد دریافت فایل اصل مقاله | 91,775,668 |
تخمین ابعاد حفرۀ آبشستگی در پاییندست سرریز لبهتیز مرکب مثلثی-مثلثی با استفاده از مدلهای هوش مصنوعی | ||
| تحقیقات مهندسی سازه های آبیاری و زهکشی | ||
| دوره 26، بهار - شماره پیاپی 98، اردیبهشت 1404، صفحه 84-67 اصل مقاله (982.46 K) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22092/idser.2025.369369.1618 | ||
| نویسندگان | ||
| ُساسان نجاتی1؛ مهدی مفتاح هلقی* 2؛ عبدالرضا ظهیری3؛ یونس امینپور4 | ||
| 1دانشجوی دکتر ای سازه های آبی ، گروه مهندسی آب، دانشگاه علوم کشاورزی و منابع طبیعی گرگان، گرگان، ایران | ||
| 2گروه مهندسی آب، دانشکده مهندسی آب و خاک، دانشگاه علوم کشاورزی و منابع طبیعی گرگان، گرگان، ایران | ||
| 3گروه مهندسی آب ، دانشکده آب و خاک، دانشگاه علوم کشاورزی و منابع طبیعی گرگان، گرگان، ایران | ||
| 4پژوهشکده مهندسی هیدرولیک و محیط هایآبی، موسسه تحقیقات آب وزارت نیرو، تهران، ایران. | ||
| چکیده | ||
| سرریزهای مرکب بهمنظور افزایش ظرفیت تخلیه جریان و رفع محدودیتهای سرریزهای ساده در دبیهای بالا طراحی میشوند. یکی از مهمترین چالشها و تهدیدهایی که در پایداری این سازهها مطرح میشود، احتمال وقوع آبشستگی در پاییندست این نوع سرریزهاست. ازاینرو، بررسی و پیشبینی عمق و محل حفرۀ آبشستگی اهمیت بالایی دارد. در این تحقیق، در آزمایشگاه روی سرریز مرکب مثلثی – مثلثی مطالعه گردید. سرریز مورد مطالعه، پس از ساخت در فلوم آزمایشگاهی نصب و نیمرخهای آبشستگی در پاییندست سازه، در دبیهای مختلف استخراج گردید. پارامترهای موثر بر آبشستگی در پاییندست سرریزهای مرکب مثلثی-مثلثی شناسایی گردیدند و ابعاد حفرۀ آبشستگی با بهرهگیری از مدلهای هوشمصنوعی از جمله شبکۀ عصبی، شبکۀ عصبی-فازی تطبیقی و شبکه ماشین بردار پشتیبان تخمین زده شد. تحلیل حساسیت روی پارامترهای موثر بر ابعاد حفرۀ آبشستگی حاکی از آن است که با تغییر 10 درصد در متغیرهای ورودی، عدد فرود ذره بیشترین تاثیر را بر ابعاد حفره دارد. افزون بر این، مقایسۀ مقادیر پیشبینی شده توسط مدلهای هوش مصنوعی با دادههای آزمایشگاهی حاکی از دقت بالای مدلهای هوشمصنوعی است. نتایج بهدست آمده نشان میدهد که روش ماشین بردار پیشتیبان با کرنل RBF، با ضریب نش-ساتکلیف 0/895 بهترین عملکرد را در پیشبینی بیشینه عمق آبشستگی دارد. | ||
| کلیدواژهها | ||
| تحلیل حساسیت؛ عدد فرود ذره؛ دادههای آزمایشگاهی؛ ضریب نش-ساتکلیف؛ شبکه عصبی | ||
| عنوان مقاله [English] | ||
| Estimating Scour Hole Dimensions Downstream of Triangular-Triangular Compound Sharp-Crested Weirs Using Artificial Intelligence Models | ||
| نویسندگان [English] | ||
| Sasan Nejati1؛ Mehdi Meftah halaghi2؛ Abdolreza Zahiri3؛ Younes Aminpour4 | ||
| 1Ph.D Student of Water Structures,, Department of Water Engineering, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran | ||
| 2Water Eng. Department of water and soil Eng., Gorgan university of agricultural science and natural resources, Gorgan, Iran | ||
| 3Department of Water Engineering, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran. | ||
| 4Department of Hydraulic Structures, Water Research Institute, Ministry of Energy, Tehran, Iran. | ||
| چکیده [English] | ||
| Introduction This study investigates scour downstream of triangular–triangular compound weirs, which generate unique flow patterns leading to increased turbulence and scour. Initially, laboratory experiments were conducted to identify key influencing factors, followed by dimensional analysis to determine relevant dimensionless parameters. Three artificial intelligence algorithms Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Support Vector Machine (SVM) were then used to perform sensitivity analysis and predict scour depth. The research enables a comparative evaluation of these models in predicting complex hydraulic phenomena. Methodology This study examines the scouring phenomenon downstream of triangular-triangular compound weirs using experimental methods and artificial intelligence models. The investigated weir was constructed by combining two triangular sections with angles of 90° and 150°, installed in a 6.7-meter long and 90-centimeter wide experimental flume at the Water Research Institute of the Ministry of Energy. Tests were conducted with four different flow rates ranging from 7.08 to 54.94 liters per second using sediments with an average diameter of 0.5 mm. The scouring profile was analyzed through digital imaging and Grapher software. For the modeling section, four artificial intelligence methods were employed to predict scouring hole dimensions: ANN, ANFIS and SVM. These models were selected for their capability to identify nonlinear relationships and complex patterns in data, with their performance evaluated using RMSE, Nash-Sutcliffe efficiency (NS), and MARE metrics. This study provides a framework for comparing various AI methods in hydraulic problems and can contribute to improving the design of hydraulic structures. Results and Discussion Given the complex and uncertain nature of scour phenomena in slope control structures, as evidenced by previo research and the significant errors in conventional models, this study employed AI approaches (ANN, ANFIS and SVM) that require less understanding of underlying physical mechanisms. A Comprehensive sensitivity analysis was first conducted on dimensionless input parameters (FrD, t/t0, and ytw/yh), with all algorithms implemented in MATLAB and Python. The models were trained on 70% of experimental data and tested on 30%, while a thorough parameter sensitivity analysis was performed across all four models to enhance prediction accuracy of scour hole dimensions. In the ANN modeling approach, a sensitivity analysis of input parameters was conducted by varying each input by ±10% to evaluate their relative influence on scour hole dimensions. The results revealed that the particle Froude number had the greatest impact (45%) on maximum scour depth, while t/t0 significantly affected scour hole length (32%). The SVM model's sensitivity analysis showed that the particle Froude number had the highest average influence (55%), whereas ytw/yh had the least (10%). Similarly, ANFIS analysis indicated the particle Froude number's dominant effect (62% average) with ytw/yh again showing minimal impact. Comparative evaluation of error metrics demonstrated that the SVM model outperformed the other proposed methods, exhibiting superior accuracy and performance in predicting scour characteristics. Conclusions This study aimed to evaluate the performance of artificial intelligence models in predicting the dimensions of the scour hole downstream of a triangular–triangular compound weir, based on laboratory data. Three AI models: Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Support Vector Machine (SVM) were assessed. The results showed that all three models were capable of estimating key scour parameters, but the SVM model outperformed the others in terms of higher accuracy, lower relative error, and better agreement with experimental data. From a hydraulic perspective, the findings indicated that the particle Froude number was one of the most influential parameters on scour dimensions. As the Froude number increases, both the maximum scour depth and scour hole length significantly increase, highlighting the critical role of flow energy in scour development downstream of compound weirs. The results also showed that under concentrated flow conditions and with increasing relative time, the scour hole continued to expand, indicating a progressive development process. Based on these results, it is recommended that in the design of compound weirs especially triangular–triangular types the effects of the Froude number and inflow conditions should be carefully considered to prevent the formation of deep scour holes. Furthermore, the SVM model, as a precise predictive tool, can assist hydraulic structure designers in evaluating scour risk and implementing appropriate protective measures (e.g., protective aprons, stepped profiles, or more resistant materials) before construction. These findings can contribute to improving the safety of hydraulic structures, reducing scour-related damage, and optimizing future designs. | ||
| کلیدواژهها [English] | ||
| Sensitivity analysis, Particle Froude number, Experimental data, Nash-Sutcliffe efficiency (NS), Neural network | ||
| مراجع | ||
|
Aminpour, Y., and Farhoudi, J. (2015). Investigation of local scour phenomenon downstream of stilling basins in the presence of steppe spillway. Journal of Hydraulics, (9)4. 25–38. (In Persian) Cortes, C., and Vapnik, V. (1995). Support-vector networks. Machine learning, 20(3), 273-297. Chen, S. T., and Yu, P. S. (2007). Real-time probabilistic forecasting of flood stages. Journal of Hydrology, 340(1-2), 63-77. Daneshfaraz, R., Bagherzadeh, M., Esmaeeli, R., Norouzi, R., & Abraham, J. (2021). Study of the performance of support vector machine for predicting vertical drop hydraulic parameters in the presence of dual horizontal screens. Water Supply, 21 (1): 217–231. Daneshfaraz, R., Mehrivar, E., Aminvash, E., & Rezaie, M. (2025). Experimental investigation of scouring parameters of the downstream bed of simple and gabion rostral drops. AQUA - Water Infrastructure, Ecosystems and Society; 74 (4): 335–348. Foddis, M. L., Ackerer, P., Montisci, A. and Uras, G. (2015), Ann-Based Approach for the Estimation Aquifer Pollutant Source Behaviour, Water Science and Technology, Water Supply, 15(6), 1285-1294. Göğüş, M., Defne, Z., and Özkandemir, V. (2006). Broad-crested weirs with rectangular compound cross sections. Journal of Irrigation and Drainage Engineering, (132) 3, 272–280. Govindaraju, R. S. (2000), Artificial Neural Networks in Hydrology. Ii: Hydrologic Applications, Journal of Hydrologic Engineering, 5, 124-137. Haykin, S. and Lippmann, R. (1994), Neural Networks, a Comprehensive Foundation, International Journal of Neural Systems, 5, 363-364. Jang, J.-S. R., Sun, C.-T. and Mizutani, E. (1997), Neuro-Fuzzy and Soft Computing; a Computational Approach to Learning and Machine Intelligence, Prentice-Hall. Khorami, E., Heidari, M., and Ghobadian, R. Estimation of downstream scour in non-cohesive materials and sensitization of factors affecting it in ski-jump using neural network and laboratory model. Irrigation and Drainage Structures Engineering Research, (23)86, 111-132. (In Persian) Li, X. and Tsai, F. T.-C. (2009), Bayesian Model Averaging for Groundwater Head Prediction and Uncertainty Analysis Using Multimodel and Multimethod, Water resources research, 45(9). Martínez, J., Reca, J., Morillas, M. T., and López, J. G. (2005). Design and calibration of a compound sharp-crested weir. Journal of Hydraulic Engineering, (131)2, 112–116. Majedi-Asl, M., Omidpour Alavian, T., Seyfari, Y., & Kouhdaragh, M. (2024). Modeling of discharge Coefficient of nonlinear weirs with QNET and SVM methods, Journal of Hydraulic Structures, 10(2): 30-45. Mousavi, S. (2023). Estimating the scour depth of slope control structures with sharp-crested weir using artificial intelligence models. Water Resources, (15)55, 105-118. (In Persian) Nourani, V., Mousavi, S., Sadikoglu, F., & Singh, V. P. (2017). Experimental and AI-based numerical modeling of contaminant transport in porous media. Journal of contaminant hydrology, )205(, 78-95. Nourani, V., Mogaddam, A. A. and Nadiri, A. O. (2008), An Ann-Based Model for Spatiotemporal Groundwater Level Forecasting, Hydrological Processes, (22), 5054-5066. Obaida A., Khattab N., & Mohammed A (2023). Scour depth downstream sharp-crested weir. Journal of Engineering and Applied Science, 70(1), 1-11. Singh, R. M. and Datta, B. (2007), Artificial Neural Network Modeling for Identification of Unknown Pollution Sources in Groundwater with Partially Missing Concentration Observation Data, Water Resources Management, (21), 557-572. Rehbock, T.(1929), Discussion of Precise Measurements, Trans of ASCE. 93: p. 1143- 1162. Roshanghar, K., & Rohparvar, B. (2013). Evaluation of Artificial Intelligence Systems for Simulation of Bridge Piers Scouring in Cohesive Soils. Water and Soil Science, 23(3), 169-182. (In Persian) | ||
|
آمار تعداد مشاهده مقاله: 385 تعداد دریافت فایل اصل مقاله: 186 |
||