پديد آورندگان :
پاپي، محمد دانشگاه وليعصر عج - گروه مهندسي عمران، رفسنجان , خجسته فر، احسان دانشگاه وليعصر عج - گروه مهندسي عمران، رفسنجان , ناصر علوي، صادق دانشگاه وليعصر عج - گروه مهندسي عمران، رفسنجان
كليدواژه :
طراحي احتمالي بر مبناي عملكرد , بهينه سازي سازه‌ها , منحني شكنندگي , فروريزش , بهينه سازي اندازه شبكه عصبي مصنوعي , تحليل ديناميكي افزايشي
چكيده فارسي :
فلسفه سنتي طراحي لرزه اي سازه ها بر مبناي نيروي اينرسي ناشي از زلزله در حال جايگزيني با فلسفه طراحي احتمالي بر مبناي عملكرد ميباشد كه در اين ديدگاه منحنيهاي شكنندگي نقش مهمي را دارا هستند. منحنيهاي شكنندگي بيانگر احتمال ايجاد سطحي از آسيب (حالت حدي) در برابر تاثير شدتي از زلزله (پارامتر شدت) مي باشند. در اين مقاله مسئله بهينه سازي وزن سازه با لحاظ نمودن قيود احتمالي (احتمال فروريزش هدف) بررسي شده است. به اين منظور، و براي عملي نمودن حل مسئله بهينه سازي، احتمال فروريزش سازه نمونه با استفاده از شبكه عصبي مصنوعي آموزش ديده پيش بيني شده است. علاوه بر قيد احتمال فروريزش سازه؛ قيود تعيني (شامل ماكزيمم تنش و ماكزيمم تغيير مكان نسبي) با استفاده از تحليل ماتريسي سازه مورد مطالعه، در مسئله بهينه سازي دخيل شده اند. بهينه سازي وزن سازه با استفاده از الگوريتم ژنتيك صورت گرفته شده است. در نهايت اثر مقدار احتمال فروريزش هدف، بر حاكم بودن معيار در سازه بهينه به دست آمده بررسي شده است. نتايج نشان ميدهند كه با در نظر گرفتن احتمال فروريزش بيش از 10% براي سازه نمونه مورد مطالعه معيارهاي تعيني حاكم بر وزن سازه بهينه خواهند بود و براي احتمال فروريزش هدف كمتر از اين مقدار، قيد آسيب احتمالي حاكم بر طرح نهايي خواهد بود.
چكيده لاتين :
Force-based seismic design, as the conventional earthquake resistant design philosophy, is going to be replaced with probabilistic performance-based design methodology. Through this method, induced damages against various levels of strong ground motions, play a dominant role. Seismic-induced damages are characterized by probabilistic damage functions, namely fragility curves. Fragility curves show the probability of exceeding damage levels (i.e. limit states) conditioned on strong ground motion intensities (i.e. Intensity Measures). Amongst well-known limit states (such as Immediate Occupancy, Life Safety and Collapse Prevention) for which the structure is to be checked, sidesway collapse limit state is of the greatest importance owing to the large amount of triggered losses during past earthquakes. Incremental Dynamic Analysis (IDA) method is the most popular method to achieve fragility curves for variuos limit states. Through this methodology, the structure is affected by increasing levels of ensemble of strong ground motions. For each ground motion, the intensity which causes the instability of finite element model of the structure presents the collapse points. Fitting log-normal probability distribution to the achieved intensities presents collapse fragility curves. The structure is to be checked against sidesway collapse in such a way that the probability of collapse for design-level seismic hazard is less than the pre-defined allowable probability.
Optimization of structures is aimed to present the topology, shape of structures and size of structural sections in such that minimum target function (mostly the structural weight) is achieved, while variuos design constraints are satisfied. Size optimization of structural members has been accomplished through previuos researches applying gravity and equivalent lateral forces while seismic effects are taken into consideration. Besides to achieve the optimum structures applying the physical effects of earthquakes, number of researches applied time history analysis of structures against one earthquake record or mean of number of earthquake records. To involve the effects of uncertainties regarding strong ground motions, probabilistic damage margins must be included in the optimization constraints. To achieve this goal, in this paper, weight optimization of structres considering probabilistic constraints (represented by the target collapse probability) is investigated. To achieve an efficient algorithm, the collapse fragility curve of structure is predicted by trained neural network. The network is trained based on incremental dynamic analysis of simulated models of samped structures. Besides probabilistic constraint regarding collapse probability margin, maximum normal stress and inter-story drift ratio (as the deterministic constraints) are involved. Deterministic constraints are calculated by matrix analysis of the structure. The neural networks are trained to predict mean and standard deviation values of collapse fragility curves assuming modeling parameters as input neurons. Genetic algorithm is applied to solve the optimization problem for which the collapse probability for population of structures is predicted through the trained neural network. Finally, the effects of target collapse probability on the achieved optimum weight are examined.
Achieved results show that the probabilistic constraint governs the optimization problem if the target probability of collapse is less than 10%. Beyond this value, deterministic constraints, which are the maximum normal stress and interstory drift ratio governs the optimum weight of the sampled structure.
