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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 | ||
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