پیوندهای مفید
آمار
| تعداد نشریات | 7 |
| تعداد شماره‌ها | 410 |
| تعداد مقالات | 5,457 |
| تعداد مشاهده مقاله | 5,872,498 |
| تعداد دریافت فایل اصل مقاله | 5,231,155 |
طراحی بهینه شبکه دفع آب‌های سطحی بر پایه تحلیل ریسک با تلفیق الگوریتم ژنتیک و مدل SWMM | ||
| نشریه مهندسی عمران امیرکبیر | ||
| دوره 54، شماره 5، مرداد 1401، صفحه 1903-1924 اصل مقاله (1.91 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22060/ceej.2021.19990.7308 | ||
| نویسندگان | ||
| سونیا صادقی* 1؛ جمال محمدولی سامانی2؛ حسین محمد ولی سامانی3 | ||
| 1دانشجوی دکترا سازه های آبی دانشگاه تربیت مدرس تهران ایران | ||
| 2هیات علمی دانشگاه تربیت مدرس | ||
| 3استاد گروه مهندسی عمران، دانشگاه شهید چمران اهواز | ||
| چکیده | ||
| طراحی شبکه دفع آب های سطحی کاری پرهزینه است؛ بنابراین طراحی باید به نحوی انجام شود که هزینه آن حداقل گردد. این عمل نیازمند مدل‌سازی در قالب یک مسئله بهینه‌سازی است. طراحی سیستم مرتبط با سیلاب، با ریسک همراه است و لذا طرحی بهینه است که هر دو جنبه هزینه اجرا و خسارت احتمالی در آینده را مدنظر قرار دهد. انتخاب دوره بازگشت بارش-رواناب در این مقاله بر اساس تحلیل ریسک صورت گرفته است. در این روش دوره بازگشتی انتخاب میشود که در آن مجموع هزینه های طرح و ریسک خسارت حداقل است. نرم‌افزار SWMM برای شبیه‌سازی هیدرولوژیکی-هیدرولیکی شبکه استفاده گردید. بهینه‌سازی شبکه با استفاده از الگوریتم ژنتیک انجام شد. متغیرهای تصمیم گیری شامل قطر و شیب لوله‌ها است. به منظور محاسبه هزینه خسارت ناشی از رواناب روابطی برای کاربری‌ها، زیرساخت‌ها، فضای سبز و ترافیک ارائه شد. صحت و دقت مدل شبیه‌سازی – بهینه‌سازی برای طراحی بهینه شبکه دفع آب های سطحی، با ارزیابی آن در شبکه محک تأیید گردید. اجرای مدل مذکور در یکی از نواحی شهر تهران برای تعیین دوره بازگشت بهینه طراحی با رویکرد تحلیل ریسک انجام شد. نتایج نشان داد که دوره بازگشت بهینه که در آن مجموع هزینههای طراحی و هزینه ریسک خسارت حداقل باشد، دوره بازگشت 10 ساله با هزینه ریسک خسارت سالانه 508/68 میلیارد ریال، هزینه طراحی سالانه 943/78 میلیارد ریال و هزینه کل 1452/45 میلیارد ریال است؛ بنابراین تلفیق الگوریتم ژنتیک و مدل SWMM و با در نظر گرفتن رویکرد طراحی مبتنی بر ریسک، مجموعه ای کارآمد است که قادر به طراحی بهینه شبکه دفع آب های سطحی است. | ||
| کلیدواژه‌ها | ||
| الگوریتم ژنتیک؛ مدل SWMM؛ تحلیل ریسک؛ خسارت رواناب؛ بهینه‌سازی | ||
| موضوعات | ||
| سیلاب شهری | ||
| عنوان مقاله [English] | ||
| Optimal Design of Storm Sewer Network Based on Risk Analysis by Combining Genetic Algorithm and SWMM Model | ||
| نویسندگان [English] | ||
| sonia sadeghi1؛ jamal samani2؛ hossein samani3 | ||
| 1PhD student in Water Structures, Tarbiat Modares University, Tehran, Iran | ||
| 2Faculty of Tarbiat Modares University | ||
| 3Professor of Shahid Chamran University | ||
| چکیده [English] | ||
| The design of an urban storm sewer network is a costly task. Therefore, the design should be done so that the total cost becomes minimal. This requires modeling the problem in an optimization form. Floods are stochastic. Designing such a system is associated with risk. Thus, a project is optimal when both design costs and potential future risks are incorporated. This means that the selection of rainfall-runoff return period has to be based on risk analysis. SWMM software was used to handle hydraulic network simulation and the Network optimization was performed using a genetic algorithm in which the decision variables were the diameter and slope of the pipes. To calculate the cost of run-off damage, relationships for land uses, infrastructure and traffic were provided. The accuracy of the simulation-optimization model seeking the optimal design of the storm sewer network was approved by a benchmark network evaluation. The developed model was implemented in a region of Tehran city to determine the optimal design return period with a risk analysis approach. The results showed that the 10-year return period with is the optimal return period with an annual damage risk cost of 508.68 billion rials, an annual design cost of 943.78 billion rials and a total cost of 1452.45 billion rials. Therefore, the developed method in which the genetic algorithm and SWMM model are combined in addition to the risk-based design approach is an effective tool for the optimal design of storm sewer networks. | ||
| کلیدواژه‌ها [English] | ||
| Genetic Algorithm, SWMM Model, Risk Analysis, Runoff Damage, Optimization | ||
| مراجع | ||
|
[1] S. Lukman , A.S. Abdurrasheed, Improvement Design of Storm Sewer Network for Flood Control. Frontiers in Environmental Engineering (FIEE), 2(3) (2013) 31-37. [2] H. Yu, G. Huang, C. Wu, Application of the stormwater management model to a piedmont city: a case study of Jinan City, China, Water Sci Technol, 70(5) (2014) 858-864. [3] Z. Zhu, Z. Chen, X. Chen, P. He, Approach for evaluating inundation risks in urban drainage systems, Science of The Total Environment, 553 (2016) 1-12. [4] R. Shariat, A. Roozbahani, A. Ebrahimian, Risk Assessment of the Urban Runoff Collection Networks Using Spatial Multi Criteria Decision-Making (Case Study: District 11 of Tehran), Journal of Water and Wastewater; Ab va Fazilab ( in persian ), 30(1) (2019) 1-17. [5] D. Dutta, S. Herath, K. Musiake, A mathematical model for flood loss estimation, Journal of Hydrology, 277(1) (2003) 24-49. [6] A.Danandemehr, Measuring and evaluating the amount of damage caused by floods. 10th Civil Engineering Student Conference, Amir Kabir University of Technology (2010). (In Persian). [7] H. Daliran Firouz, F. Mokhtari, S. Soltani, S.A. Mousavi, Flood Damage Assessment in Ghamsar and Ghohrood Watershed Basins Using HEC-FIA, Journal of Water and Soil Science, 19(74) (2016) 63-76. (In Persian). [8] J.Huizinga, H.Moel, W.Szewczyk, Global flood depth-damage functions. Methodology and the database with guidelines. EUR 28552 EN. (2017). [9] S. Mahmood, A.-u. Rahman, Flash flood susceptibility modeling using geo-morphometric and hydrological approaches in Panjkora Basin, Eastern Hindu Kush, Pakistan, Environmental Earth Sciences, 78(1) (2019) 43. [10] A. Mehrvarz, A.Madadi, F.Esfandiari, M.Rahimi, Simulation of floods in the Aort Valley using the HEC-RAS hydraulic model in GIS environment (study area: from Shuristan village to the confluence of Aras river). Quantitative Geomorphology Research, 8(4) (2020)131-146. (In Persian). [11] J. Sin, C. Jun, J.H. Zhu, C. Yoo, Evaluation of Flood Runoff Reduction Effect of LID (Low Impact Development) based on the Decrease in CN: Case Studies from Gimcheon Pyeonghwa District, Korea, Procedia Engineering, 70 (2014) 1531-1538. [12] L.Rossman, Storm Water Management Model User’s Manual Revised, Version 5.0, EPA No. EPA/600/R-05/040. (2010) (295 p). [13] G.C. Dandy, A.R. Simpson, L.J. Murphy, An Improved Genetic Algorithm for Pipe Network Optimization, Water Resources Research, 32(2) (1996) 449-458. [14] S. Yagi, S. Shiba, Application of genetic algorithms and fuzzy control to a combined sewer pumping station, Water Science and Technology, 39(9) (1999) 217-224. [15] A.R. Simpson, G.C. Dandy, L.J. Murphy, Genetic Algorithms Compared to Other Techniques for Pipe Optimization, Journal of Water Resources Planning and Management, 120(4) (1994) 423-443. [16] Z.Y. Wu, A.R. Simpson, Competent Genetic-Evolutionary Optimization of Water Distribution Systems, Journal of Computing in Civil Engineering, 15(2) (2001) 89-101. [17] S.E. Cieniawski, J.W. Eheart, S. Ranjithan, Using Genetic Algorithms to Solve a Multiobjective Groundwater Monitoring Problem, Water Resources Research, 31(2) (1995) 399-409. [18] B.J. Ritzel, J.W. Eheart, S. Ranjithan, Using genetic algorithms to solve a multiple objective groundwater pollution containment problem, Water Resources Research, 30(5) (1994) 1589-1603. [19] M.H. Afshar, M. Rohani, Optimal design of sewer networks using cellular automata-based hybrid methods: Discrete and continuous approaches, Engineering Optimization, 44(1) (2012) 1-22. [20] A. Mousavi ,Optimization of storm sewer networks in flat areas with heavy storms based on performance indices. A thesis submitted in partial fulfillment of the requirement for the Ph.D. degree (2016) (In Persian). [21] H. M.V. Samani, Design of hydraulic structures. Dez Ab Consulting Engineering Publications.346P (2016) (In Persian). [22] L.W. Mays, J.C. Liebman, H.G. Wenzel, Model for Layout and Design of Sewer Systems, Journal of the Water Resources Planning and Management Division, 102(2) (1976) 385-405. [23] M.H. Afshar, Application of a Genetic Algorithm to Storm Sewer Network Optimization, Scientia Iranica, 13(3) (2006) 234-244. [24] J. P. Heaney, D.Sample L.Wright, C.Fan, Costs of urban storm water control, US Environmental Protection Agency, Office of Research and Development, National Risk Management Research Laboratory, Washington, DC, EPA/600/R-02/021 (NTIS PB2003-103299),(2002). [25] M.H. Afshar, A parameter free Continuous Ant Colony Optimization Algorithm for the optimal design of storm sewer networks: Constrained and unconstrained approach, Advances in Engineering Software, 41(2) (2010) 188-195. [26] S.W. Miles, J.P. Heaney, Better Than “Optimal” Method for Designing Drainage Systems, Journal of Water Resources Planning and Management, 114(5) (1988) 477-499. [27] M.H.A. Afshar, Rebirthing particle swarm optimization algorithm: application to storm water network design, Canadian Journal of Civil Engineering, 35(10) (2008) 1120-1127. [28] Technical and Civil Engineering Department of Tehran.Tehran Surface Water Management Comprehensive Plan, Summary of Studies Report,11 (2012) 269 pages. (In Persian). [29] L.Jaf, Flood Damage Assessment Using HEC FDA Software in Khansar Watershed. M.Sc. Thesis Department of Natural Resources. Isfahan University of Technology (2015) (In Persian). [30] H.Hekmati Far, Optimal combination of engineering and flood management methods Case study: Qarasu River in Kermanshah. M.Sc. Thesis, Department of Natural Disaster Management, University of Tehran. (2006) (In Persian). [31] K.Amirmoradi, A.Shokohi, A.Azizian, Evaluating Risk of Economic Loss due to River Flood in Urban areas (Study Area: Kan Watershed). Iran J Soil Water Res.50 (9) (2019) 2239-2259(In Persian). [32] P.Swamee, A.Sharma,design of water supply pipe networks. Published by John Wiley & Sons, Inc. Hoboken, New Jersey(2008).362P. | ||
|
آمار تعداد مشاهده مقاله: 864 تعداد دریافت فایل اصل مقاله: 1,180 |
||