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تخمین پاسخهای فروریزش سازه با در نظر گرفتن عدم قطعیتهای مدلسازی با استفاده از روش سطح پاسخ و شبکه عصبی مصنوعی | ||
| نشریه مهندسی عمران امیرکبیر | ||
| مقاله 3، دوره 53، شماره 6، شهریور 1400، صفحه 2249-2276 اصل مقاله (2.87 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22060/ceej.2020.17312.6539 | ||
| نویسندگان | ||
| محمد امین بیاری1؛ اسماعیل ایزدی زمان آبادی* 2؛ ناصر شابختی3 | ||
| 1دانشکده مهندسی عمران، واحد نجف آباد، دانشگاه آزاد اسلامی، نجف آباد، ایران | ||
| 2گروه سازه و زلزله، دانشکده مهندسی عمران، واحد نجف آباد، دانشگاه آزاد اسلامی، نجف آباد، ایران | ||
| 3دانشکده مهندسی عمران، دانشگاه علم و صنعت ایران، تهران، ایران | ||
| چکیده | ||
| در این تحقیق ارزیابی پاسخهای فروریزش یک سازه قاب خمشی بتنی با در نظر گرفتن عدم قطعیتهای مدلسازی مورد بررسی قرار گرفته است. عدم قطعیتهای مدلسازی برای ارزیابی پاسخ فروریزش، پارامترهای مربوط به منحنی ممان- چرخش اصلاح شده ایبارا-کراوینکر در تیرها و ستون های سازه میباشد. برای آنالیز عدم قطعیت، همبستگی بین پارامترهای مدل در یک جز و بین پارامترهای دو جز سازهای در نظر گرفته شده است. برای تولید متغیرهای تصادفی مستقل از روش LHS و از تجزیه چولسکی برای ایجاد متغیرهای تصادفی وابسته استفاده شده است. با تولید 281 شبیه سازی برای عدم قطعیتها با در نظر داشتن همبستگی بین آنها، آنالیزهای دینامیکی افزایشی با 44 شتابنگاشت دور از گسل انجام شده است. پاسخهای فروریزش برای هر شبیهسازی شامل میانگین ظرفیت فروریزش، میانگین دریفت فروریزش و میانگین بسامد سالیانه فروریزش به دست آمده و سپس با استفاده از روش سطح پاسخ و شبکههای عصبی پاسخهای فروریزش پیشبینی شده است. نتایج نشان میدهند که مقادیر ضریب همبستگی بین دادههای هدف حاصل از تحلیلهای دینامیکی افزایشی و دادههای خروجی حاصل از پیشبینی، به روش سطح پاسخ و شبکه عصبی مصنوعی برای پاسخهای فروریزش بالای0/98 به دست آمده است و حداکثر خطای پیشبینی برای میانگین ظرفیت فروریزش و میانگین دریفت فروریزش کمتر از 5% و برای میانگین بسامد سالیانه سالیانه فروریزش کمتر از 10% در روش سطح پاسخ و شبکه عصبی مصنوعی میباشد. | ||
| کلیدواژهها | ||
| آنالیز عدم قطعیت؛ آنالیز دینامیکی افزایشی؛ پاسخ فروریزش سازه؛ شبکه عصبی مصنوعی؛ روش سطح پاسخ | ||
| موضوعات | ||
| رفتار لرزه ای؛ رفتار لرزه ای سازه بتنی؛ قابلیت اطمینان | ||
| عنوان مقاله [English] | ||
| Estimating Structural Collapse Responses Considering Modeling Uncertainties using Artificial Neural Networks and Response Surface Method | ||
| نویسندگان [English] | ||
| Mohammad Amin Bayari1؛ Esmaeel Izadi Zaman Abadi2؛ Naser Shabakhty3 | ||
| 1Department of Civil Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran | ||
| 2Department of Civil Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran | ||
| 3School of Civil Engineering, Iran University of Science and Technology, Tehran, Iran | ||
| چکیده [English] | ||
| This research investigates the collapse responses of a concrete moment frame considering modeling uncertainties. These modeling uncertainties are considered for evaluating a collapse response related to the modified Ibarra-Krawinkler moment-rotation parameters for beam and column elements of a given structure. To analyze these uncertainties, the correlations between the model parameters in one component and between two structural components were considered. Latin Hypercube Sampling (LHS) method was employed to produce independent random variables. Moreover, Cholesky decomposition was adopted to produce correlated random variables. Performing 281 simulations for the uncertainties involved considering their inter-correlations, incremental dynamic analysis (IDA) was done using 44 far-field accelerograms to determine structural collapse responses. Collapse responses of each simulation, including mean collapse capacity, mean collapse drift and mean annual frequency, were obtained. Then, the collapse responses were predicted using the response surface method and artificial neural network. The results show that the Correlation coefficients (R) between the target data resulted from incremental dynamic analysis (IDA), output data resulted from response surface method (RSM), and artificial neural network (ANN) were obtained for the collapse responses above 0.98. The maximum prediction errors for mean collapse capacity and mean collapse drift are less than 5% and for mean annual frequency less than 10% under the response surface method (RSM) and artificial neural network (ANN). | ||
| کلیدواژهها [English] | ||
| Uncertainty analysis, Incremental dynamic analysis, Structural Collapse Responses, Response Surface Method, Artificial Neural Network | ||
| مراجع | ||
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