توسعه منحنی های شکنندگی فروریزش با در نظرگرفتن عدم قطعیت های مدل سازی با استفاده از شبیه سازی LHS و شبکه های عصبی مصنوعی

نوع مقاله : علمی - پژوهشی

نویسندگان

1 دانشجوی دکتری سازه، دانشکده مهندسی عمران، واحد نجف آباد، دانشگاه آزاد اسلامی، نجف آباد، ایران

2 استادیار، دانشکده مهندسی عمران، دانشگاه علم و صنعت ایران، تهران، ایران

3 استادیار، دانشکده مهندسی عمران، واحد نجف آباد، دانشگاه آزاد اسلامی، نجف آباد، ایران

10.22065/jsce.2020.187419.1871

چکیده

ارزیابی عملکرد فروریزش سازه ها به دلیل پیچیدگی این پدیده و وجود عدم قطعیت های مدل سازی برای شبیه سازی پاسخ فروریزش سازه ها همواره مورد توجه محققین بوده است. مدل های مفصل پلاستیک متمرکز به عنوان بهترین کاندید برای مدل سازی رفتار فروریزش سازه ها مورد استفاده قرار می گیرد. منحنی های شکنندگی فروریزش تحت تاثیر منابع مختلف عدم قطعیت قرار دارند. در این مطالعه عدم قطعیت های موجود در پارامترهای مدل ممان چرخش اصلاح شده ایبارا-کراوینکلر در سازه های قاب خمشی بتنی به عنوان عدم قطعیت های مدل سازی مورد استفاده قرار گرفته و برای آنالیز عدم قطعیت از روش شبیه سازی LHS برای تولید متغیرهای تصادفی با در نظر گرفتن همبستگی بین عدم قطعیت های مدل سازی در یک جز و بین دو جز سازه ای استفاده شده است. با تولید نمونه های تصادفی برای عدم قطعیت ها با استفاده از آنالیزهای دینامیکی افزایشی پاسخ های فروریزش یعنی میانگین ظرفیت فروریزش و انحراف استاندارد فروریزش برای هر شبیه سازی بدست آمده است. با توجه به تلاش محاسباتی بسیار بالا استفاده از تحلیل های دینامیکی افزایشی، جهت تخمین و پیش بینی پاسخ های فروریزش سازه از شبکه های عصبی مصنوعی MLP، شبکه عصبی GMDH و روش سطح پاسخ استفاده شده است و میزان خطای هر روش بدست آمده است. نتایج نشان می دهند که استفاده از روش های نامبرده باعث ایجاد پیش بینی هایی با دقت بسیار بالا و خطای کمتر از 10% برای شبکه عصبی GMDH و خطای کمتر از 7% برای شبکه عصبی MLP  و روش سطح پاسخ می شود.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Collapse Fragility Curves Development with Considering of Modeling Uncertainties Using LHS Simulation and Artificial Neural Network

نویسندگان [English]

  • Mohammad Amin Bayari 1
  • Naser Shabakhty 2
  • Esmaeel Izadi Zaman Abadi 3
1 Ph.D. Candidate, Department of Civil Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran
2 Assistant Professor, School of Civil Engineering, Iran University of Science and Technology, Tehran, Iran
3 Assistant Professor, Department of Civil Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran
چکیده [English]

Collapse performance evaluation of structures has been a concern for researchers due to its complexity and uncertainty in modeling and simulation. Concentrate plastic hinges are best candidates for modeling collapse behavior of structures. Collapse fragility curves are affected by various sources of uncertainty. Existing uncertainties in modified Ibarra and Krawinkler moment-rotation model for concrete moment frame buildings were investigated in this paper. LHS simulation method was used to generate random variables considering the correlation among modeling uncertainties in one component and two structural components. Collapse responses including mean collapse capacity and standard deviation were obtained for each simulation by generating random samples for uncertainties using incremental dynamic analysis (IDA). As much effort is needed for implementation of IDA, MLP artificial neural networks, GMDH artificial neural network and response surface method were used to estimate and anticipate the collapse behavior of the structure. Results show that using above methods will lead to high accuracy anticipations with an error of less than 10% for GMDH neural network and an error of less than 7% for MLP and response surface methods.

کلیدواژه‌ها [English]

  • Collapse fragility curves
  • modeling uncertainties
  • LHS simulation
  • Artificial Neural Network
  • response surface method
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