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کاربرد روش های بهبود الگوریتم بهینه‌سازی فراکاوشی برای طراحی پل‌های بتنی مسلح در مقیاس واقعی: مطالعه موردی تقاطع انهر کمربندی ارومیه | ||
| علوم و فنون سازندگی | ||
| دوره 6، شماره 1 - شماره پیاپی 18، مرداد 1404، صفحه 51-61 اصل مقاله (903.71 K) | ||
| نوع مقاله: مقاله پژوهشی | ||
| نویسندگان | ||
| سینا شیرگیر* 1؛ پویا آقابیگی2 | ||
| 1دانشکده مهندسی عمران، دانشگاه تبریز | ||
| 2مدیریت دانش موسسه عاشورا، تبریز، ایران | ||
| تاریخ دریافت: 27 خرداد 1402، تاریخ بازنگری: 09 مرداد 1402، تاریخ پذیرش: 09 مهر 1402 | ||
| چکیده | ||
| یادگیری مبتنی بر تضاد (OBL) یک رویکرد موثر برای بهبود عملکرد الگوریتم‌های بهینه‌سازی فراکاوشی است که معمولا برای حل مسائل مهندسی پیچیده استفاده می‌شود. در این مقاله استراتژی یادگیری متضاد برای ترکیب با الگوریتم بهینه‌سازی ملخ (GOA) ارائه می‌شود. در این الگوریتم پیشنهادی، راه‌حل‌های تولید شده توسط الگوریتم GOA مرتب شده و به دو راه‌حل خوب و بد تقسیم می‌شوند، سپس راه‌حل‌های بد انتخاب می‌شوند تا با استفاده از آموزش برمبنای تضاد، راه حل های جدید تولید شود. برای تأیید قابلیت الگوریتم پیشنهادی، برخی از توابع ریاضیاتی معیار آزمایش شدند. علاوه بر این، عملکرد الگوریتم OGOA با اجرای یک طراحی بهینه از یک پل بتن مسلح با مقیاس واقعی ارزیابی شد. برای شناسایی پارامترهای موثر در طراحی اجزای سازه‌ای پل‌های بتن مسلح، تحلیل حساسیت انجام شده است. علاوه بر این، هزینه کل مصالح در ستون‌های پایه‌ها و عرشه پل به عنوان یک تابع هدف تعریف شد. همچنین ابعاد مقاطع و میلگردهای فولادی طولی به عنوان متغیرهای طراحی انتخاب می‌شوند. نتایج شبیه‌سازی‌ها پایداری و استحکام روش OGOA پیشنهادی را در مقایسه با GOA استاندارد نشان می‌دهد. همچنین الگوریتم پیشنهادی OGOA در طراحی بهینه ستون‌های و عرشه پل‌های بتنی مسلح یک روش کارآمد می‌باشد. | ||
| کلیدواژه‌ها | ||
| پل‌های بتنی مسلح؛ بهینه‌سازی؛ الگوریتم بهینه سازی ملخ؛ آموزش برمبنای تضاد | ||
| عنوان مقاله [English] | ||
| The application of improvement methods in the meta-heuristic algorithm for the design of reinforced concrete bridges in real scale: a case study of Urmia ring road | ||
| نویسندگان [English] | ||
| Sina Shirgir1؛ Pouya Aghabeigi2 | ||
| 1Faculty of Civil Engineering- University of Tabriz | ||
| 2Knowledge management, Ashura institute, Tabriz, Iran | ||
| چکیده [English] | ||
| Opposition-based learning (OBL) is an effective approach to improve the performance of meta-heuristic optimization algorithms that are commonly used to solve complex engineering problems. In this paper, an opposite learning strategy is presented to combine with the Grasshopper Optimization Algorithm (GOA). In this proposed algorithm (OGOA), the solutions generated by the GOA algorithm are sorted and divided into good and bad solutions, then the bad solutions are selected to generate new solutions using opposition-based learning. To verify the performance of the proposed algorithm, some benchmark mathematical functions were tested. In addition, the performance of the OGOA algorithm was evaluated by implementing an optimal design of a real-scale reinforced concrete bridge. To identify the effective parameters in the design of structural components of reinforced concrete bridges, sensitivity analysis has been performed. In addition, the total cost of materials in the foundation columns and bridge deck was defined as an objective function. Also, the dimensions of sections and longitudinal steel bars are selected as design variables. The simulation results show the stability and robustness of the proposed OGOA method compared to the standard GOA. Also, the proposed OGOA is an efficient method for the optimal design of columns and decks of reinforced concrete bridges. | ||
| کلیدواژه‌ها [English] | ||
| Reinforced concrete bridges, Optimization, Grasshopper optimization algorithm, Opposition-based learning | ||
| مراجع | ||
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آمار تعداد مشاهده مقاله: 250 تعداد دریافت فایل اصل مقاله: 268 |
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