بهینه ‏سازی قاب‏ های بتن آرمه بر اساس عملکرد با استفاده از الگوریتم ‏های فراکاوشی و شبکۀ عصبی

نوع مقاله : مقاله پژوهشی

نویسنده

گروه عمران، دانشکدۀ فنی خوی، دانشگاه ارومیه، خوی، ایران

چکیده

هدف اصلی در بهینه ‏سازی قاب‏های بتن آرمه براساس عملکرد، کاهش هزینه های ساخت با الزام ارضای قیدهای دریفت طبقات و چرخش مفاصل پلاستیک اعضا می ‏باشد. در این تحقیق از الگوریتم‏ های فراکاوشی اجتماع ذرات، برخورد اجسام، کرم شب تاب، کلونی مورچگان و خفاش، برای بهینه ‏سازی قاب‏ های بتن آرمۀ 3 و 6 طبقه براساس عملکرد استفاده شده، نتایج حاصله از الگوریتم‏ های فوق با هم مقایسه شده‏ اند. بهینه سازی سازه ‏های بتن آرمه، بسیار پیچیده ‏تر از سازه ‏های فولادی می‏ باشد. علت این امر، وجود اندازه ‏های مختلف برای ابعاد اعضا و آرایش ‏های متفاوت برای آرماتورگذاری می‌باشد. در این تحقیق با توجه به هزینۀ محاسباتی بالای ارزیابی عملکرد لرزه ‏ای سازه‌ها، برای افزایش سرعت محاسبات و کاهش زمان عملیات، از شبکه ‏های عصبی استفاده شده است. نتایج عددی، عملکرد مناسب‏ تر الگوریتم برخورد اجسام در مقایسه با سایر الگوریتم‏ های فراکاوشی را نشان می‏ دهد.

کلیدواژه‌ها


عنوان مقاله [English]

Performance-based Optimization of Reinforced Concrete Frames by Means of Meta-Heuristic Algorithms & Neural Network

نویسنده [English]

  • Masood Danesh
Department of Civil Engineering Khoy Faculty of Engineering Urmia University Khoy Iran
چکیده [English]

The mean objective of performance based optimization of reinforced concrete frames (RC) is to reduce the cost of construction by requiring the satisfaction of the inter-story drifts and rotation of the plastic joints of the members. In this research, two 3 & 6 stories RC performance-based optimized by Particle Swarm (PSO), Enhanced Colliding Bodies (ECBO), firefly Algorithm (FA),Ants Colony (ACO) and Bat (BAT) meta-heuristic algorithms, then compare results with together. Optimization of RC is much complicated than Steel frames, because different dimensions of members & configuration of reinforcing. Due to the high cost of seismic performance evaluation of structures, in this research, neural networks used to increase the computational speed & reduce the operating time. Numerical results show the proper performance of the ECBO in comparison with other meta-heuristic algorithms.Also, the results of different algorithms do not show much difference.For further evaluation of the results, it is recommended to Calculate its Collapse Margin Ratios.

کلیدواژه‌ها [English]

  • Performance-based optimization of RC
  • Particle swarm optimization
  • Ants colony optimization
  • Bat algorithm
  • Enhanced colliding bodies optimization
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