School of Mechanical and Electrical Engineering, Xi'an Polytechnic University, Xi'an, China.
School of Mechanics, Civil Engineering and Architecture, Northwestern Polytechnical University, Xi'an, China.
Sci Prog. 2021 Jan-Mar;104(1):368504211003385. doi: 10.1177/00368504211003385.
Fixed beam structures are widely used in engineering, and a common problem is determining the load conditions of these structures resulting from impact loads. In this study, a method for accurately identifying the location and magnitude of the load causing plastic deformation of a fixed beam using a backpropagation artificial neural network (BP-ANN). First, a load of known location and magnitude is applied to the finite element model of a fixed beam to create plastic deformation, and a polynomial expression is used to fit the resulting deformed shape. A basic data set was established through this method for a series of calculations, and it consists of the location and magnitude of the applied load and polynomial coefficients. Then, a BP-ANN model for expanding the sample data is established and the sample set is expanded to solve the common problem of insufficient samples. Finally, using the extended sample set as training data, the coefficients of the polynomial function describing the plastic deformation of the fixed beam are used as input data, the position and magnitude of the load are used as output data, a BP-ANN prediction model is established. The prediction results are compared with the results of finite element analysis to verify the effectiveness of the method.
固定梁结构在工程中得到了广泛的应用，其中一个常见的问题是确定这些结构在承受冲击载荷时的受力状态。在本研究中，采用反向传播人工神经网络（BP-ANN）方法来准确识别导致固定梁发生塑性变形的载荷的位置和大小。首先，将已知位置和大小的载荷施加到固定梁的有限元模型上，以产生塑性变形，并使用多项式表达式来拟合所得的变形形状。通过这种方法建立了一个基本数据集，用于一系列计算，该数据集由施加的载荷的位置和大小以及多项式系数组成。然后，建立了一个用于扩展样本数据的 BP-ANN 模型，并通过扩展样本集来解决样本不足的常见问题。最后，使用扩展的样本集作为训练数据，将描述固定梁塑性变形的多项式函数的系数作为输入数据，将载荷的位置和大小作为输出数据，建立 BP-ANN 预测模型。将预测结果与有限元分析的结果进行比较，验证了该方法的有效性。
