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Research on static finite element model revision based on neural network

机译:基于神经网络的静态有限元模型修订研究

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A updating method of the structure static model based on the neural network is introduced. It specifies the application of the neural network algorithm in the revision of the static finite element model. It adopts the method of numerical simulation static load experiment to build a BP network model, which a complex non-linear relationship exists between the physical parameters and boundary conditions of a three-span continuous box girder structure and deflections. The finite element calculation which its input data substituted by inverse simulation data is consistent with the assumptions of the ‘real’ data. It proved the feasibility and practicality of this method.
机译:介绍了基于神经网络的结构静态模型的更新方法。 它指定了神经网络算法在静态有限元模型的修订中的应用。 它采用数值模拟静态负载实验的方法来构建BP网络模型,其中三跨连续箱梁结构和偏转的物理参数和边界条件之间存在复杂的非线性关系。 通过逆模拟数据代替的有限元计算与&#x2018的假设是一致的;真实的’ 数据。 它证明了这种方法的可行性和实用性。

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