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首页> 外文学位 >Analysis of building wind pressure using proper orthogonal decomposition, autoregressive moving average and neural networks.
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Analysis of building wind pressure using proper orthogonal decomposition, autoregressive moving average and neural networks.

机译：使用适当的正交分解，自回归移动平均值和神经网络来分析建筑风压。

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摘要

The proper orthogonal decomposition (POD), autoregressive moving average (ARMA) models, and neural networks are used in the analysis of wind-induced pressure on buildings.;The POD offers a unique way of representing a random field such as wind pressure. The pressure space covariance is employed to calculate the modes and the principal coordinates of the considered data. These quantities are then applied in a selective reconstruction of the pressure.;A procedure for the POD of pressure specified at non-uniformly spaced locations is described in detail. The numerical integration called for by the POD is performed using the rectangular rule and Lagrange's formulation. The POD results, the eigenvalues and eigenfunctions (modes), for the pressure at uniformly distributed taps are compared with those for the pressure at non-uniformly distributed taps.;The ARMA is used to model both the wind-induced pressure and the POD principal coordinates. Several model selection criteria are employed in the optimization of the model order. The model order is used in comparison of complexity of the pressure and the POD principal coordinates.;The wind-induced pressure are used to train neural networks and to evaluate the performance of the developed neural network. Several configurations of the backpropagation neural network are tested by varying the number of nodes in the input layer and the number of neurons in the hidden layer. The effects of the number of hidden layers as well as the sampling frequency of the time series on the performance of the neural network are also investigated.;The POD is found to be useful in compression of large sets of wind pressure data, while preserving pertinent features of the considered pressure field. The AR models established in this study can be used to generate wind-induced pressure on buildings and structures. The accuracy of the one-step prediction of the wind-induced pressure using the neural network is found to be compatible with that of the AR model.
机译：适当的正交分解（POD），自回归移动平均（ARMA）模型和神经网络用于分析建筑物上的风致压力。POD提供了一种表示随机字段（例如风压）的独特方法。压力空间协方差用于计算所考虑数据的模式和主坐标。然后将这些量应用到压力的选择性重建中。详细描述在非均匀间隔位置指定的压力POD的过程。使用矩形规则和拉格朗日公式执行POD要求的数值积分。将均匀分布抽头压力的POD结果，特征值和特征函数（模式）与不均匀分布抽头压力的POD结果进行比较.ARMA用于模拟风压和POD原理坐标。在优化模型顺序时采用了几种模型选择标准。模型顺序用于比较压力和POD主坐标的复杂性。风压用于训练神经网络和评估已开发神经网络的性能。通过改变输入层中节点的数量和隐藏层中神经元的数量，测试了反向传播神经网络的几种配置。还研究了隐藏层的数量以及时间序列的采样频率对神经网络性能的影响。;发现POD可用于压缩大型风压数据集，同时保留相关信息考虑的压力场的特征。在这项研究中建立的AR模型可用于在建筑物和结构上产生风压。发现使用神经网络对风压的一步预测的准确性与AR模型的准确性是兼容的。

著录项

  • 作者

    Jeong, Seung-Hwan.;

  • 作者单位

    Colorado State University.;

  • 授予单位 Colorado State University.;
  • 学科 Engineering Civil.;Computer Science.
  • 学位 Ph.D.
  • 年度 1998
  • 页码 155 p.
  • 总页数 155
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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