基于自适应神经模糊系统的结构可靠性和灵敏度分析

A. Ghorbani, M. Ghasemi
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引用次数: 1

摘要

本研究采用自适应神经模糊推理系统(ANFIS)和蒙特卡罗仿真方法对结构进行可靠性分析。蒙特卡罗模拟的缺点是计算量大。ANFIS能够近似结构响应来计算失效概率,使计算负担大大降低。事实上,ANFIS自适应地推导出隐式极限状态函数的显式逼近。为此,采用拟灵敏度分析方法确定了主要设计变量,得到了结构失效概率的近似解。然而,在制备ANFIS之前,使用了一种基于松弛的方法,该方法可以获得最佳的训练样本数量和epoch。这是为了更有效地减少ANFIS训练的计算时间。采用一些说明性的例子审议了所提议的方法。
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Reliability and Sensitivity Analysis of Structures Using Adaptive Neuro-Fuzzy Systems
In this study, Adaptive Neuro-Fuzzy Inference System (ANFIS) and Monte Carlo simulation are applied for reliability analysis of structures. The drawback of Monte Carlo Simulation is the amount of computational efforts. ANFIS is capable of approximating structural response for calculating probability of failure, letting the computation burden at much lower cost. In fact, ANFIS derives adaptively an explicit approximation of the implicit limit state functions. To this end, a quasi-sensitivity analysis in consonance with ANFIS was developed for determination of dominant design variables, led to the approximation of the structural failure probability. However, preparation of ANFIS , was preceded using a relaxation-based method developed by which the optimum number of training samples and epochs was obtained. That was introduced to more efficiently reduce the computational time of ANFIS training. The proposed methodology was considered applying some illustrative examples.
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来源期刊
Journal of Rehabilitation in Civil Engineering
Journal of Rehabilitation in Civil Engineering Engineering-Building and Construction
CiteScore
1.60
自引率
0.00%
发文量
0
审稿时长
12 weeks
期刊最新文献
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