بهینه سازی مقطع سدهای انحرافی با استفاده از الگوریتم بهینه سازی ازدحام ذرات
محورهای موضوعی : هیدرولوژی، هیدرولیک و ساختمان های انتقال آبشهاب نادری 1 , سعید شعبانلو 2 , محمد رضا جواهری تفتی 3 , بهروز یعقوبی 4
1 - دانشجوي دکتري عمران، گروه مهندسي عمران، واحد تفت، دانشگاه آزاد اسلامي ، تفت، ايران.
2 - گروه مهندسي آب، واحد کرمانشاه، دانشگاه آزاد اسلامي، کرمانشاه، ايران.
3 - گروه مهندسي عمران، واحد تفت، دانشگاه آزاد اسلامي، تفت، ايران.
4 - گروه مهندسي آب، واحد کرمانشاه، دانشگاه آزاد اسلامي، کرمانشاه، ايران.
کلید واژه: بهينه‌سازي ازدحام ذرات, پايداري, سد انحرافي نازليان,
چکیده مقاله :
مقدمه و هدف پژوهش: طراحي هيدروليکي سدهاي انحرافي به طور سنتي بسيار پيچيده و زمان‌بر است و لازم است طراح چندين بار مفروضات استفاده شده را تغيير دهد تا به طراحي پايدار با حجم بتن‌ريزي مناسب دست يابد. در دنياي پيشرفته و پررقابت امروزي، به دليل کمبود مواد اوليه و نياز به راندمان بهتر، مهندسان طراح مجبور به طراحي بهينه و اقتصادي‌تر هستند. بنابراين لازم است هزينه بتن در طراحي اين سدها کاهش يابد و در عين حال پايداري سد نيز تضمين شود. در اين تحقيق، کاربرد يک الگوريتم فرا ابتکاري به منظور کمينه‌سازي تابع وزن سد انحرافي و در نتيجه هزينه‌ي بتن‌ريزي سد همزمان با تآمين تابع‌هاي مربوط به پايداري سد بررسي شده است.
مواد و روش ها: الگوريتم بکار گرفته شده در اين پژوهش الگوريتم بهينه‌سازي ازدحام ذرات (PSO) مي‌باشد. با توجه به ماهيت اين الگوريتم و مسئله‌ي مورد پژوهش، ايجاد تغييرات در مراحل اجرايي الگوريتم ضروري است. لذا با ايجاد تغييراتي در الگوريتمPSO، اين الگوريتم براي حل مسئله‌ي بهينه‌سازي وزن سد با درنظر گرفتن محدوديت‌هاي اين مسئله، توسعه داده شد. اين تغييرات شامل کنترل سرعت ذرات جستجوگر و نرمال‌سازي موقعيت ذرات در فضاي امکان‌پذير مسئله مي‌باشد. سد انحرافي مورد مطالعه در اين پژوهش سد نازليان واقع در استان کرمانشاه است. متغيرهاي تصميم در مسئله‌ي بهينه‌سازي شامل ارتفاع ديوار آب بند بالادست و پايين‌دست، طول و ضخامت کف بند بتني در بالادست و ضخامت حوضچه آرامش مي‌باشد. به منظور بهينه‌سازي اين ابعاد، با انجام تحليل حساسيت، پارامترهاي با بيشترين تاثيرگذاري بر روي عملکرد الگوريتم مشخص شدند.
نتايج و بحث: در اين پژوهش براساس نتايج حساسيت‌سنجي تعداد ذرات يا اندازه‌ي جمعيت الگوريتم PSO، تعداد 20 ذره براي اندازه‌ي جمعيت ذرات انتخاب شد. با توجه به نمودارهمگرايي الگوريتم PSO در اجراهاي مختلف، براي اطمينان از يافتن پاسخ بهينه سراسري و اجتناب از محاسبات و ارزيابي‌هاي اضافي، تعداد تکرار الگوريتم برابر با 1000 به دست آمد. با توجه به بهترين اجراي الگوريتم PSO، وزن سد مورد مطالعه در پاسخ بهينه‌ي اين الگوريتم برابر با 80/52 تن در واحد عرض سد به دست آمد. بهترين پاسخ به مسئله طراحي بهينه سد داراي ابعاد 6/7 متر براي طول کف‌بند بالادست، 6/0 متر براي ضخامت کف‌بند بالادست، 6/0 متر براي ضخامت حوضچه آرامش، 1 متر براي ارتفاع آببند بالادست و 1/1 متر براي ارتفاع آببند پايين‌دست بود.
نتيجه‌گيري: به طور کلي نتيجه‌ي پژوهش حاضر حاکي از عملکرد و سرعت مطلوب الگوريتم PSO در يافتن پاسخ بهينه‌ي مسئله‌ي طراحي سد انحرافي با کمترين وزن سد همراه با رعايت شاخص‌هاي پايداري مي‌باشد. استفاده از اين الگوريتم به منظور يافتن پارامترهاي بهينه براي طراحي سدهاي انحرافي مي‌تواند اطلاعات مفيدي را در اختيار مديران اجرايي قرار دهد تا بهترين و بهينه‌ترين طراحي را با حداقل هزينه و زمان بسيار کم با رعايت فاکتورهاي ايمني پايداري سدهاي انحرافي ارائه دهند. برنامه تدوين شده در اين تحقيق شرايط لازم و کافي را براي طراحي بهينه مقطع سدهاي انحرافي فراهم مي کند و از نظر اعتبار عمومي و عملي قابل اعتماد است اما داراي محدوديت‌هايي است. از جمله محدوديت‌هاي اين روش اين است که مقطع به‌دست‌آمده بر اساس کد توسعه‌يافته در اين مطالعه بايد بعداً براي معيارهاي تنش مختلف (با استفاده از روش اجزاي محدود) و براي شرايط خاص که در هر پروژه متفاوت است (زلزله، سيل، رسوب‌گذاري و غيره) مورد آزمايش قرار گيرد.
Introduction: The hydraulic design of diversion dams is traditionally very complicated and time-consuming and it is necessary for the designer to change the used assumptions several times in order to achieve a stable design with a suitable concreting volume. In today's advanced and competitive world, due to the lack of raw materials and the need for better efficiency, design engineers are forced to design more economically and optimally. Therefore, it is necessary to reduce the cost of concrete in the design of these dams and at the same time ensure the stability of the dam. In this research, the application of a meta-heuristic algorithm has been investigated in order to minimize the weight function of the diversion dam and, as a result, the cost of concreting the dam at the same time as providing the functions related to the dam's stability.
Methods: The algorithm used in this research is the particle swarm optimization (PSO) algorithm. According to the nature of this algorithm and the research problem, it is necessary to make changes in the execution steps of the algorithm. Therefore, by making changes in the PSO algorithm, this algorithm was developed to solve the dam weight optimization problem by considering the limitations of this problem. These changes include controlling the speed of searcher particles and normalizing the position of particles in the feasible space of the problem. The diversion dam studied in this research is the Nazelian dam located in Kermanshah province. The decision variables in the optimization problem include the height of the upstream and downstream dam cutoff walls, the length and thickness of the concrete apron at upstream and the thickness of the detention pond. In order to optimize these dimensions, by performing sensitivity analysis, the parameters with the greatest impact on the performance of the algorithm were identified.
Results and Discussion: In this research, based on the results of the sensitivity measurement of the number of particles or the population size of the PSO algorithm, the number of 20 particles was chosen for the size of the population of particles. According to the convergence graph of the PSO algorithm in different executions, to ensure finding the optimal global response and avoid additional calculations and evaluations, the number of iterations of the algorithm was equal to 1000. According to the best implementation of the PSO algorithm, the weight of the studied dam in the optimal response of this algorithm was equal to 52.80 tons per unit of dam width. The best answer to the problem of optimal design of the dam has dimensions of 7.6 meters for the length of the upstream apron, 0.6 meters for the thickness of the upstream apron, 0.6 meters for the thickness of the detention pond, 1 meter for the height of the upstream cutoff and 1.1 meters for the height of the downstream cutoff.
Conclusion: In general, the result of the present research indicates the optimal performance and speed of the PSO algorithm in finding the optimal solution to the problem of designing a diversion dam with the least weight of the dam along with the observance of stability indicators. Using this algorithm in order to find the optimal parameters for the design of diversion dams can provide useful information to the executives to provide the best and most optimal design with minimum cost and very little time while observing the safety factors of the stability of diversion dams. The program developed in this research provides the necessary and sufficient conditions for the optimal design of the cross-section of diversion dams and is reliable in terms of general and practical validity, but it has limitations. Among the limitations of this method is that the cross-section obtained based on the code developed in this study must be adjusted later for different stress criteria (using the finite element method) and be tested for specific conditions that are different in each project (earthquake, flood, sedimentation, etc.).
Abdollahi, A., Amini, A. and Hariri-Ardebili, M. A. 2022. An uncertainty-aware dynamic shape optimization framework: Gravity dam design. Reliability Engineering & System Safety. 222: 108402. https://doi.org/10.1016/j.ress.2022.108402
Afshar, MH., Afshar, A., Marino, MA., & Hon, M. (2009). An Iterative Penalty Method for the Optimal Design of Pipe Networks. International Journal of Civil Engineering, 7(2), 109-123. (In Persion)
Aljuboori, M. and Datta, B. 2019. Improved optimal design of concrete gravity dams founded on anisotropic soils utilizing simulation-optimization model and hybrid genetic algorithm. ISH Journal of Hydraulic Engineering. 27(2):1-18. https://doi.org/10.1080/09715010.2019.1574614
Amiri Tokaldany, A. (1996). TABDAM software for designing of diversion dams. In: Proceedings of the 8th Conference National Committee on Irrigation and Drainage, Tehran, Iran, pp. 31-58. (In Persion)
Araujo, L. J., Vieira, A. and Gutstein, D. 2019. Optimization Study For The Cross-Section Of A Concrete Gravity Dam: Genetic Algorithm Model And Application. Revista Internacional de Metodos Numericos para Calculo y Diseno en Ingenieria. 35(3): 1-8. https://doi.org/10.23967/j.rimni.2019.06.002
Aslani, M., Emadi, AR., & Nazarpour, H. (2013). Determination of appropriate values of genetic algorithm parameters in optimization of gravity dams cross section. Journal of Water and Soil Conservation, 20(5), 231-239. (In Persion)
Bayrami, M. K., 1401. Water Conveyance Structures. 15th edition. Isfahan University of Technology Press. 476 pages. (In Persion).
Carlisle, A. & Dozier, G. (2001). An Off-The-Shelf PSO. Proc. of the Particle Swarm Optimization Workshop, 1-6.
Chen, Z. Q., Wu, H. and Cheng, Y. H. 2024. Dynamic behaviors of concrete gravity dam against combined blast wave and bubble pulsation of underwater explosion. Ocean Engineering. 302: 117677. https://doi.org/10.1016/j.oceaneng.2024.117677
Deepika, R., & Suribabu, CR., (2015). Optimal design of gravity dam using differential evolution algorithm. Iran University of Science & Technology, 5(3), 255-266.
Deshmukh, M. A. and Magar R. B. 2022. Gravity dam analysis using particle swarm optimization method. Materials Today: Proceedings. 69 (3): 1233-1237. https://doi.org/10.1016/j.matpr.2022.08.295
Engelbrecht, AP. (2007). Computational intelligence: an introduction. John Wiley & Sons.
Ferdowsi, A., Mousavi, SF., Farzin, S., & Karami, H. (2020). Optimization of dam's spillway design under climate change conditions. Journal of Hydroinformatics, 22(4), 916-936.
Garsole, P. A., Bokil, S., Kumar, V., Pandey, A. and Topare, N. S. 2023. A review of artificial intelligence methods for predicting gravity dam seepage, challenges and way-out. Water Infrastructure, Ecosystems and Society. 72 (7): 1228–1248. https://doi.org/10.2166/aqua.2023.042
Ghasemi, M. R., Salarnia, A. and Ghasri, M. 2024. Optimal Design of Steel Structures Using Innovative Black Widow Algorithm Hybridized with Greedy Sensitivity-Based Particle Swarm Optimization Technique. Journal of Soft Computing in Civil Engineering. 8(1): 55-84. https://doi.org/10.22115/SCCE.2023.370472.1568
Ghiasi, V., Alborzi Moghadam, M. and Koushki, M. 2022. Optimization of Invasive Weed for Optimal Dimensions of Concrete Gravity Dams. Journal of Soft Computing in Civil Engineering. 6(4): 95-111. https://doi.org/10.22115/SCCE.2022.340697.1432
Habibi, A., Zarei, S. and Khaledy, N. 2021. Optimum design of concrete gravity dams using sequential quadratic programming algorithm. Dams and Reservoirs. 31(1): 10-20. https://doi.org/10.1680/jdare.21.00003
Heidari, M M. (2006). Development of a perfect software for designing diversion dams, including: the computations of hydraulic, stability and economic aspects and hydraulic design of intake and sluiceway. M.SC. dissertation, University of Tehran. (In Persion).
Kennedy, J. and Eberhart, R. 1995. Particle Swarm Optimization. Proceedings of the IEEE International Conference on Neural Networks, 4, 1942-1948. http://dx.doi.org/10.1109/ICNN.1995.488968
Khatibinia, M. and Khosravi, S. 2014. A hybrid approach based on an improved gravitational search algorithm and orthogonal crossover for optimal shape design of concrete gravity dams. Applied Soft Computing. 16: 223-233. https://doi.org/10.1016/j.asoc.2013.12.008
Khoramshokooh, N., Veiskarami, M., Nikoo, MR. & Pourvahedi Roshandeh, S. (2018). Multi-objective hydraulic optimization of diversion dam’s cut-off. Water Resources Management, 32(11), 3723-3736.
Lane, E. W. 1935. Security from under-seepage masonry dams on earth foundations, Transactions of the American Society of Civil Engineers. 100 (1). https://doi.org/10.1061/TACEAT.0004655
Li, SH., Jing, L., & Zhou, IJ. (2010). The shape optimization of concrete gravity dam based on GA-APDL. In International Conference on Measuring Technology and Mechatronics Automation, 982–986. China.
Lin, G., Wang, Y., & Hu, Z. (2010). Hydrodynamic pressure on arch dam and gravity dam including absorption effect of reservoir sediments OP Conf. Series: Materials Science and Engineering, 19–23 July, Sydney,Australia, 1-10.
Mattias, L.W.A. and Araujo, L.J. 2023. Optimization of reinforced concrete columns with variable circular cross-section hollowed using the criterion of instability and mechanical strength. Multidiscipline Modeling in Materials and Structures. 19(4): 604-616. https://doi.org/10.1108/MMMS-01-2023-0017
Moghadam, RG., Shabanlou, S., Yosefvand, F. (2020). Optimization of ANFIS Network Using Particle Swarm Optimization Modeling of Scour around Submerged Pipes. J. Marine. Sci. Appl. 19, 444–452.
Noorzad, R., & Rezaeian, A. (2011). Optimizing the cross-section of earthen dams with ant colony optimization algorithm. The 6th National Congress of Civil Engineering. Semnan University. Iran. (In Persian)
Nourani, B., Salmasi, F. and Ghorbani, M. A. 2023. Development of a new hybrid technique for estimating of relative uplift force in gravity dams based on whale optimization algorithm. Journal of Hydraulic Structures. 9 (23): 43-62. https://doi.org/10.22055/JHS.2023.41889.1230
Nourmohammadi Dehbalaei, F., Azari, A. and Akhtari, A. A.. 2023. Development of a linear–nonlinear hybrid special model to predict monthly runof in a catchment area and evaluate its performance with novel machine learning methods. Applied Water Science, 13 (5), 1-23. https://doi.org/10.1007/s13201-023-01917-2
Pirzadeh, B., Jafari-asl, J., Mohtashami, A. and Ohadi, S. 2022. Determination the optimal dimensions of concrete gravity dam by using metaheuristic algorithms (Comparison of algorithms). Journal of Irrigation and Water Engineering. 12 (46): 203-222. https://doi.org/10.22125/IWE.2021.142105
Qi, G. (2012). Optimized program design of gravity dam section. In: Proceedings of the International Conference on Modern Hydraulic Engineering, 9-11 Mar., Nanjing, China, 419-423.
Salmasi, F. and Abraham, J. P. 2022a. Computation of Optimal Cross Section of Gravity Dams Using Genetic Algorithms. Current Overview on Science and Technology Research. 6: 1-23. https://doi.org/10.9734/bpi/costr/v6/4048A
Salmasi, F. and Abraham, J. P. 2022b. Drainage Gallery in Concrete Gravity Dams and Its Effect on Reduction of Uplift Forces. Novel Perspectives of Engineering Research. 10: 43-62. https://doi.org/10.9734/bpi/nper/v10/2222B
Sartipi, N., Salmasi, F., Abraham, J. and Dalir A. H. 2021. Investigation of the effect of depth and distance between cutoff walls on uplift force for gravity dams. International Journal of Environmental Science and Technology. 18, 1361–1378. https://doi.org/10.1007/s13762-020-02867-x
Shahbazbigi, E., shabanlou, S., & Izadbakhsh, M. (2018). Modeling the Length of Hydraulic Jump on Sloping Rough Bed Using Hybrid Model based on Adaptive Neuro-Fuzzy Inference Systems and Particle Swarm Optimization. Iranian Water Researches Journal, 12(3), 109-118.
Simoes, LMC., & Lapa, JAM. (1994). Optimal shape of dams subject to earthquakes, Proc. Of International conference on computational structures thechnology, Advances in structural optimization, 119 - 130.
Simoes, LMC. (1995). Shape optimization of dams for static and dynamic loading, International course on hydroelectric power plants, Coimbra, Portugal.
Sobhkhiz Foumani, R., Mardookhpour, A. and Saberi. S. 2023. Optimization of time and temperature of dam construction for thermal analysis of roller compacted concrete dam. Numerical Methods in Civil Engineering. 7(4): 1-11. https://doi.org/10.61186/NMCE.2022.599.1
Soltani., K., and Azari, A. 2024. Terrestrial water storage anomaly estimating using machine learning techniques and satellite-based data (a case study of Lake Urmia Basin). Irrigation and Drainage, 73 (1), 215-229. https://doi.org/10.1002/ird.2863
Taheri Aghdam, A., Salmasi, F., Abraham, J. Arvanaghi, H. 2021. Effect of Drain Pipes on Uplift Force and Exit Hydraulic Gradient and the Design of Gravity Dams Using the Finite Element Method. Geotechnical and Geological Engineering 39, 3383–3399. https://doi.org/10.1007/s10706-021-01699-x
Wang, Y., Liu, Y. and Ma, X. 2021. Updated Kriging-Assisted Shape Optimization of a Gravity Dam. Water 13(1):87. https://doi.org/10.3390/w13010087
Wu, X., Qie, Z., Zhou, Z., & Zhang, H. (2008). Application of improved PSO to optimization of gravity dam and sluice gate. In: Proceedings of the 7thWorld Congress on Intelligent Control and Automation, 25-27 Jun., Chongqing international convention, Chongqing, China, pp. 6178-6182
Yousefi, S., Heidari, MM., & Adib Rad, MH. (2017). Optimization of Diversion Dam Section Based on Genetic Algorithm. Iranian Journal of Soil and Water Research, 47(4), 839-848. (In persion).
Yosefvand, F., Shabanlou, S., & Kardar, S. (2019). ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM OPTIMIZATION USING PSO FOR PREDICTING SEDIMENT TRANSPORT IN SEWERS. International Journal of Optimization in Civil Engineering, 9(2), 331-342.