| 基于神经网络的大跨度屋盖非高斯风压场模拟方法 |
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| 引用本文： | 孙芳锦,张爱社. 基于神经网络的大跨度屋盖非高斯风压场模拟方法[J]. 郑州大学学报(工学版), 2011, 0(4) |
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| 作者姓名： | 孙芳锦 张爱社 |
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| 作者单位： | 同济大学土木工程防灾国家重点实验室;辽宁工程技术大学建筑工程学院;山东建筑大学土木工程学院; |
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| 基金项目： | 国家自然科学基金资助项目(51078225); 辽宁工程技术大学博士启动基金(09139) |
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| 摘 要： | 采用径向基函数神经网络(Radical Basis Function Neutral Networks,简称RBF神经网络)来模拟大跨度结构的非高斯风压场.根据某大跨度结构的形式特点,将结构风场看成是屋面位置和时间的函数,将风压场分解为一系列径向基函数.再利用单调非线性无记忆转换映射和RBF中获得的风场函数定义向量过程,从而将非高斯场的模拟转换为互相关高斯过程的模拟.将RBF神经网络应用于一大跨度屋盖的非高斯场模拟,得到结构上非高斯风压场的分布.结果对比表明,RBF神经网络模拟非高斯风压场具有较高的准确性.该方法可直接利用RBF神经网络的输出结果,避免推导高斯过程和非高斯过程的关系式,因此具有较高的效率.RBF神经网络模拟非高斯风压场在准确性和效率上均具有显著优势.
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| 关 键 词： | RBF神经网络 大跨度结构 非高斯过程 风压场模拟 转换向量过程 |
Non-Gaussian Wind Field Simulation Method for Large-span Roofs Based on Neural Networks |
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| SUN Fang-jin,,ZHANG Ai-she. Non-Gaussian Wind Field Simulation Method for Large-span Roofs Based on Neural Networks[J]. Journal of Zhengzhou University: Eng Sci, 2011, 0(4) |
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| Authors: | SUN Fang-jin ZHANG Ai-she |
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| Affiliation: | SUN Fang-jin1,2,ZHANG Ai-she3(1.State Key Laboratory for Disaster Reduction in Civil Engineering,Tongji University,Shanghai 200092,China,2.Institute of Civil Engineering & Architecture,Liaoning Technical University,Fuxin 123000,3.School of Civil Engineering,Shandong Jianzhu University,Jinan 250101,China) |
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| Abstract: | Radical basis function neural networks(RBF neural networks for short) are adopted to simulate numerically non-Gaussian wind field of large-span roofs.According to properties of a large-span roof,the wind field is considered as the function of position and time,decomposed into a series of radical basis functions.And monotonic nonlinear memoryless transformation mapping and wind field function obtained from RBF neural networks are combined to define a transformation vector process,with which non-Gaussian proc... |
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| Keywords: | RBF neural network large-span roof non-Gaussian process wind field simulation transformation vector process |
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