Failure of infilled frames - a study using artificial neural network

K.M. Mini; K. Subramanian

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Failure of infilled frames - a study using artificial neural network

机译：填充框架的失效-使用人工神经网络的研究

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A neural network model to determine the failure load and drift of infilled frames under lateral loading is developed in the present paper. The backpropagation neural network is used to evaluate the failure criteria on the infilled frames using the analytically generated data. Training of the network is done by considering the aspect ratio, number of bays, area of column, area of beam, grade of concrete, grade of steel used for the construction and a non-dimensional parameter λh as the input parameters. To validate the efficacy of the model, an experimental investigation was carried out and the results are compared with that obtained using the ANN model. The experimentation is carried out under the same conditions used for the generation of the analytical data. The agreement was found to be good.

机译：建立了确定侧向荷载作用下框架失效荷载和位移的神经网络模型。反向传播神经网络用于使用分析生成的数据评估填充框架的破坏准则。网络的训练是通过将长宽比，托架的数量，柱的面积，梁的面积，混凝土的等级，用于施工的钢的等级和无量纲参数λh作为输入参数来进行的。为了验证该模型的有效性，进行了实验研究，并将结果与使用ANN模型获得的结果进行了比较。实验在产生分析数据所用的相同条件下进行。协议被认为是好的。

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来源
《International journal of design engineering》 |2010年第1期|77-96|共20页
作者
K.M. Mini; K. Subramanian;
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作者单位

Civil Engineering Department,Amrita School of Engineering,Amrita Vishwa Vidyapeetham,Coimbatore, Tamil Nadu 641 105, India;

Civil Engineering Department,Coimbatore Institute of Technology,Coimbatore, Tamil Nadu, India;

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正文语种 eng
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evolutionary computation; infilled frame; artificial neural network; ANN; failure load; equivalent strut method; backpropagation; training; testing;

机译：进化计算填充框架人工神经网络;人工神经网络破坏负荷等效支柱法反向传播训练;测试;

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Failure of infilled frames - a study using artificial neural network

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