بررسی و مدلسازی آب شستگی پایین‌دست انحراف کننده تنه درختی در کانال مستقیم
محورهای موضوعی : مقاله پژوهشیهادی رشیدی 1 , محسن نجارچی 2 * , سید محمد میرحسینی هزاوه 3
1 - دانشجوی دکتری، مدیریت منابع آب، گروه مهندسی عمران، دانشگاه آزاد اسلامی واحد اراک، اراک، ایران
2 - دانشیار گروه مهندسی آب، واحد اراک، دانشگاه آزاد اسلامی ، اراک، ایران
3 - گروه مهندسی عمران، دانشگاه آزاد اسلامی واحد اراک، اراک، ایران
کلید واژه: شبکه عصبی, آبشستگی پایین دست, منحرف کننده تنه درخت, کانال مستقیم ,
چکیده مقاله :
آبشستگی، بستگی به ایجاد موانع در مقابل جریان آب ، مقاطع غیر هیدرولیکی ، پایه ها و پی ، دست خوردگی مصالح بستر، خاک نفوذپذیر یا نفوذناپذیر، غیر موازی بودن فونداسیون و جریان آب، نوع فعالیت رودخانه اعم از استاتیکی و یا دینامیکی، وجود آبشار و یا مانع ای که باعث به وجود آمدن آبشار در مصالح بستر طبیعی ، که باعث شسته شدن مصالح زیرین بستر می شود بستگی دارد. آبشستگی موضعی در اطراف پایه های پل بر اثر برخورد جریان با پایه و جدایی جریان از آن ایجاد میگردد. ابعاد حفره آبشستگی ایجاد شده در اطراف پایه پل به خصوصیات هیدرولیکی جریان، خصوصیات سیال، هندسه پایه و مشخصات مواد بستر بستگی دارد. شبكه بهينه پيش خورنده با الگوريتم آموزشي پس انتشار خطا با توابع انتقال سيگموئيدي براي چهار مدل ذكر شده در فصل چهارم، استفاده شده است.براي تعيين تعداد نرونها در لايه پنهان، يك و ده نرون در لايه پنهان بر حسب شاخص هاي صحت سنجي انتخاب شد. با توجه به ساختار شبكه اي يك نرون در لايه پنهان مقايسه اي بين مدل پارامترهاي بعد دار و بي بعد مؤثر در ابعاد حفره آبشستگي صورت گرفت.
Introduction: Erosion is a natural phenomenon caused by water flow over erodible surfaces in rivers and channels. Local scour, a component of morphological changes in waterways, often results from human-made structures. Experimental studies demonstrate that erosion processes driven by shear stress and flow scouring at the end of protected beds can progressively destabilize riverbeds, potentially leading to structural failure. Predicting scour hole dimensions is thus critical. Scour involves a two-phase flow (water and sediment) influenced by variables such as flow conditions, bed characteristics, time, and channel geometry. Consequently, researchers have focused on experimental investigations to understand these dynamics.
Methods: This study combines empirical observations of scour phenomena with analytical methods to evaluate erosion rates. A practical equation with high predictive capability was sought. A tree-trunk deflector model was tested in a straight flume (10 m length × 0.5 m width × 0.5 m height), elevated 1.3 m above the laboratory floor, with a bed slope of 0.001. The flume featured an iron bed/framework, glass walls, and a downstream triangular weir (90° apex angle) for flow measurement. A sliding gate controlled water depth. The fixed bed spanned 60 cm, with 17 cm of uniform sand (median grain size d<sub>50</sub> = 1 mm, standard deviation 3.1) over a 210 cm length. Bed profiles were measured using a 1 mm-accuracy depth gauge after each test.
Results: A neural network model incorporating dimensional and dimensionless parameters was developed. Sensitivity analysis identified key variables, and a modified DOT relationship (Mahdavi-Zadeh, 20XX) with adjusted C<sub>h</sub> coefficients was proposed. Nonlinear regression equations derived from Buckingham π-theorem were compared to neural network performance. Models with 10 hidden-layer neurons outperformed single-neuron models in estimating scour depth (validated via SPSS), showing higher correlation (e.g., R<sup>2</sup> > 0.9) and lower mean squared error (MSE). Scour depth and flow depth were most influential, while time significantly affected scour width.
1. Akbari Fard, S., & Akhund Ali, A. (2017). "Simulation of Sea Wave Height Using Meta-Heuristic Algorithms in the Chabahar Region." *Scientific-Research Journal of Marine Techniques, 4(3), 73-84. (Autumn 2016).
2. Amini, M., & Heidarpour, M. (2017). "Investigating the Application of Combined Collar and Slot Models in Controlling and Reducing Local Scour Around Cylindrical Bridge Pier Groups." *7th National Conference on Sustainable Agriculture and Natural Resources.
3. Rajabi, D., Karami, H., Hosseini, Kh., Mousavi, F., & Hashemi, A. A. (2015). "Estimating Optimal Parameters of the Nonlinear Muskingum Routing Model Using the Imperialist Competitive Algorithm (ICA)." *Journal of Water and Soil Science (Agricultural and Natural Resources Science and Techniques), 19(73), 321-333. (Autumn 2014).
4. Hashemi Manfar, S. A., Hosseinzadeh, F., & Pirzadeh, B. (2017). "Application of the Imperialist Competitive Algorithm (ICA) in Reservoir Operation Optimization for Maximizing Demand Supply: A Case Study of Pishin Dam." *Scientific-Research Journal of Hydraulics, 12(2), 59-67.
5. Amini. A, Mohammad. Th, (2016), "Local scour prediction around piers with complex geometry", Marine Georesources and Geotechnology Journal, p863
6. Kashani, Ali Reza, Amir Hossein Gandomi, and Mehdi Mousavi. (2016) "Imperialistic competitive algorithm: a metaheuristic algorithm for locating the critical slip surface in 2-dimensional soil slopes." Geoscience Frontiers 7, no. 1: 83-89.
7. Moussa.A.M.A, (2017), "Evaluation of local scour around bridge piers for various geometrical shapes using mathematical models", Ain Shams Engineering Journal, P10.
8. Saber, Navid Abdolhoseyni, Mahdi Salimi, and Davar Mirabbasi. (2016) "A priority list based approach for solving thermal unit commitment problem with novel hybrid genetic-imperialist competitive algorithm." Energy 117: 272-280.
9. Najafzadeh, M.; Tafarojnoruz, A.; Lim, S.Y. Prediction of local scour depth downstream of sluice gates using data-driven models. ISH J. Hydraul. Eng. 2017, 23, 195–202. [CrossRef] 30. Rajaratnam, N.; Macdougall, R.K. Erosion by Plane Wall Jets with Minimum Tailwater. J. Hydraul. Eng. 1983, 109, 1061–1064. [CrossRef]
10. Najafzadeh, M.; Lim, S.Y. Application of improved neuro-fuzzy GMDH to predict scour depth at sluice gates. Earth Sci. Inform. 2015, 8, 187–196.
11. Ali, H.M.; El Gendy, M.M.; Mirdan, A.M.H.; Ali, A.A.M.; Abdelhaleem, F.S.F. Minimizing downstream scour due to submerged hydraulic jump using corrugated aprons. Ain Shams Eng. J. 2014, 5, 1059–1069.
12. Shen, H. W., Schneider, V. R., and Karaki, S. (1969). “Local scour around bridge piers.” Proc. ASCE, 95_6_, 1919–1940.
13. Jain, S. C., and Fischer, E. E. (1979). “Scour around bridge piers at high Froude numbers.” Rep. No. FHWA-RD-79-104, Federal Highway Administration,Washington D.C.
14. Froehlich, D. C. (1989). “Local scour at bridge abutments.” Proc., 1989 National Conf. on Hydraulic Engineering, New York, 13–18.
15. Melville, B. W., and Sutherland, A. J. (1988). “Design method for local scour at bridge piers.” J. Hydraul. Eng., 114_10_, 1210–1226.
16. Jones, J. S. (1984). “Comparison of prediction equations for bridge pier and abutment scour.” Proc., Transportation Research Record, Second Bridge Engineering Conf., Vol. 2, Transportation Research Board, Washington, D.C., 202–209.
17. Johnson, P. A. (1995). “Comparison of pier-scour equations using fielddata.” J. Hydraul. Eng., 121_8_, 626–629.
18. Landers, M. N., and Mueller, D. S. (1996). “Evaluation of selected pierscour equations using field data.” Transp. Res. Rec., 1523, 186–195.
19. .Mueller, D. S. (1996). “Local scour at bridge piers in nonuniform sediment under dynamic conditions.” Ph.D. thesis, Colorado State Univ.,Fort Collins, Colo.
20. Ataie-Ashtiani, B., and Beheshti, A. A. (2006). “Experimental investigation of clear-water local scour at pile groups.” J. Hydraul. Eng.,132(10), 1100–1104.
21. .Johnson, P. A., and Ayyub, B. M. (1996). “Modelling uncertainty in prediction of pier scour.” J. Hydraul. Eng., 122(2), 66–72