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RISS 인기검색어
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- 저자 : Jalilkhani, Maysam (검색결과 2 건)
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Evaluation of seismic collapse capacity of regular RC frames using nonlinear static procedure
Maysam Jalilkhani,Ali Reza Manafpour 국제구조공학회 2018 Structural Engineering and Mechanics, An Int'l Jou Vol.68 No.6
The Incremental Dynamic Analysis (IDA) procedure is currently known as a robust tool for estimation of seismic collapse capacity. However, the procedure is time-consuming and requires significant computational efforts. Recently some simplified methods have been developed for rapid estimation of seismic collapse capacity using pushover analysis. However, a comparative review and assessment of these methods is necessary to point out their relative advantages and shortcomings, and to pave the way for their practical use. In this paper, four simplified pushover analysis-based methods are selected and applied on four regular RC intermediate moment-resisting frames with 3, 6, 9 and 12 stories. The accuracy and performance of the different simplified methods in estimating the median seismic collapse capacity are evaluated through comparisons with the results obtained from IDAs. The results show that reliable estimations of the summarized 50% fractile IDA curve are produced using SPO2IDA and MPA-based IDA methods; however, the accuracy of the results for 16% and 84% fractiles is relatively low. The method proposed by Shafei et al. appears to be the most simple and straightforward method which gives rise to good estimates of the median sidesway collapse capacity with minimum computational efforts.
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Numerical solution of beam equation using neural networks and evolutionary optimization tools
Babaei, Mehdi,Atasoy, Arman,Hajirasouliha, Iman,Mollaei, Somayeh,Jalilkhani, Maysam Techno-Press 2022 Advances in computational design Vol.7 No.1
In this study, a new strategy is presented to transmit the fundamental elastic beam problem into the modern optimization platform and solve it by using artificial intelligence (AI) tools. As a practical example, deflection of Euler-Bernoulli beam is mathematically formulated by 2nd-order ordinary differential equations (ODEs) in accordance to the classical beam theory. This fundamental engineer problem is then transmitted from classic formulation to its artificial-intelligence presentation where the behavior of the beam is simulated by using neural networks (NNs). The supervised training strategy is employed in the developed NNs implemented in the heuristic optimization algorithms as the fitness function. Different evolutionary optimization tools such as genetic algorithm (GA) and particle swarm optimization (PSO) are used to solve this non-linear optimization problem. The step-by-step procedure of the proposed method is presented in the form of a practical flowchart. The results indicate that the proposed method of using AI toolsin solving beam ODEs can efficiently lead to accurate solutions with low computational costs, and should prove useful to solve more complex practical applications.
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