پیوندهای مفید
آمار
| تعداد نشریات | 9 |
| تعداد شمارهها | 451 |
| تعداد مقالات | 5,747 |
| تعداد مشاهده مقاله | 8,186,223 |
| تعداد دریافت فایل اصل مقاله | 6,685,794 |
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 | ||
|
[1] A. Der Kiureghian, O. Ditlevsen, Aleatory or epistemic? Does it matter?, Structural Safety, 31(2) (2009) 105-112. [2] C.A. Cornell, F. Jalayer, R.O. Hamburger, D.A. Foutch, Probabilistic basis for 2000 SAC federal emergency management agency steel moment frame guidelines, Journal of structural engineering, 128(4) (2002) 526-533. [3] F. Zareian, H. Krawinkler, Assessment of probability of collapse and design for collapse safety, Earthquake Engineering & Structural Dynamics, 36(13) (2007) 1901-1914. [4] G.G. Deierlein, A.M. Reinhorn, M.R. Willford, Nonlinear structural analysis for seismic design,” NEHRP seismic design technical brief 4, 1-36, 2010. [5] B. Ugurhan, J. Baker, G. Deierlein, Uncertainty estimation in seismic collapse assessment of modern reinforced concrete moment frame buildings. Proceedings of the 10th National Conference in Earthquake Engineering, Anchorage, Alaska, 2014. [6] D. Vamvatsikos, C.A. Cornell, Incremental dynamic analysis, Earthquake Engineering & Structural Dynamics, 31(3) (2002) 491-514. [7] Federal Emergency Management Agency, FEMA 350: Recommended Seismic Design Criteria for New Steel Moment‐Frame Buildings, SAC joint Venture, Washington, DC, 2000. [8] L.F. Ibarra, H. Krawinkler, Global collapse of frame structures under seismic excitations, Pacific Earthquake Engineering Research Center Berkeley, CA, 2005. [9] C.B. Haselton, G.G. Deierlein, Assessing seismic collapse safety of modern reinforced concrete moment-frame buildings, Report No. PEER 2007/08, Pacific Earthquake Engineering Research Center, College of Engineering, University of California, Berkeley, 2008. [10] C.B. Haselton, A.B. Liel, S.T. Langes, G.G. Deirlein, Beam-column element model calibrated for predicting flexural response leading to global collapse of RC frame buildings, Report No. PEER 2007/03, Pacific Earthquake Engineering Research Center, College of Engineering, University of California, Berkeley, 2008. [11] L.F. Ibarra, R.A. Medina, H. Krawinkler, Hysteretic models that incorporate strength and stiffness deterioration, Earthquake engineering & structural dynamics, 34(12) (2005) 1489-1511. [12] D. Lignos, Sidesway collapse of deteriorating structural systems under seismic excitations, Stanford university, 2008. [13] D.G. Lignos, H. Krawinkler, Deterioration modeling of steel components in support of collapse prediction of steel moment frames under earthquake loading, Journal of Structural Engineering, 137(11) (2010) 1291-1302. [14] N.D. Lagaros, M. Fragiadakis, Fragility assessment of steel frames using neural networks, Earthquake Spectra, 23(4) (2007) 735-752. [15] M. Papadrakakis, V. Papadopoulos, N.D. Lagaros, J. Oliver, A.E. Huespe, P. Sánchez, Vulnerability analysis of large concrete dams using the continuum strong discontinuity approach and neural networks, Structural Safety, 30(3) (2008) 217-235. [16] J. Park, P. Towashiraporn, Rapid seismic damage assessment of railway bridges using the response-surface statistical model, Structural Safety, 47 (2014) 1-12. [17] E. Khojastehfar, S.B.B. Aval, K. Nasorllahzadeh, M.R. Zolfaghari, Considering effects of modeling uncertainties on collapse fragility curve by artificial neural networks, Journal of Solid and Fluid Mechanics, 4(2) (2014) 25-34. [18] E. Khojastehfar, S.B. Beheshti-Aval, M.R. Zolfaghari, K. Nasrollahzade, Collapse fragility curve development using Monte Carlo simulation and artificial neural network, Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 228(3) (2014) 301-312. [19] F.K.G. Jough, S. Şensoy, Prediction of seismic collapse risk of steel moment frame mid-rise structures by meta-heuristic algorithms, Earthquake Engineering and Engineering Vibration, 15(4) (2016) 743-757. [20] F. Karimi Ghaleh Jough, S. Beheshti Aval, Uncertainty analysis through development of seismic fragility curve for an SMRF structure using an adaptive neuro-fuzzy inference system based on fuzzy C-means algorithm, Scientia Iranica, 25(6) (2018) 2938-2953. [21] F. Karimi Ghaleh Jough, S. Şensoy, Steel Moment-Resisting Frame Reliability via the Interval Analysis by FCM-PSO Approach considering Various Uncertainties, Journal of Earthquake Engineering, 24(1) (2020) 109-128. [22] M. Gupta, L. Jin, N. Homma, Static and dynamic neural networks: from fundamentals to advanced theory, John Wiley & Sons, 2004. [23] R.H. Myers, D.C. Montgomery, C.M. Anderson-Cook, Response surface methodology: process and product optimization using designed experiments, John Wiley & Sons, 2016. [24] P. Tothong, C.A. Cornell, Probabilistic seismic demand analysis using advanced ground motion intensity measures, attenuation relationships, and near-fault effects, Pacific Earthquake Engineering Research Center, 2007. [25] J. Baker, C. Cornell, Vector-valued ground motion intensity measures for probabilistic seismic demand analysis, PEER Report 2006/08, Pacific Earthquake Engineering Research Center-College of Engineering, 2006. [26] Federal Emergency Management Agency, FEMA P-695: Quantification of Buildings Seismic Performance Factors, Federal Emergency Management Agency, Washington, DC, 2009. [27] J. Douglas, Ground motion prediction equations 1964–2018, Department of Civil and Environmental Engineering University of Strathclyde, 2018. [28] Baker Research Group, Earthquake ground motion characterization using the Conditional Spectrum, https://web.stanford.edu/~bakerjw/research/conditional_spectrum.html. [29] J.W. Baker, Conditional mean spectrum: Tool for ground-motion selection, Journal of Structural Engineering, 137(3) (2011) 322-331. [30] F. Behnamfar, M. Nooraei, M. Talebi, A 3-stage Method for Selection of Ground Motion for Dynamic Time History Analysis, Amirkabir Journal of Civil Engineering, 49(1) (2017) 127-138. (In Persian) [31] M. Ghafory-Ashtiany, M. Mousavi, A. Azarbakht, Epsilon as an indicator of ground motion spectral shape, Sharif Civil Engineering Journal, 29(4) (2014) 109-116. (In Persian) [32] T.B. Panagiotakos, M.N. Fardis, Deformations of reinforced concrete members at yielding and ultimate, Structural Journal, 98(2) (2001) 135-148. [33] Y.-K. Tung, B.C. Yen, Hydrosystems engineering uncertainty analysis, McGraw-Hill New York, 2005. | ||
|
آمار تعداد مشاهده مقاله: 1,594 تعداد دریافت فایل اصل مقاله: 2,086 |
||