Method for determining load magnitude and location from the plastic deformation of fixed beams using a neural network

• Published in: Science progress (1916) 2021-01, Vol.104 (1), p.368504211003385-368504211003385
• Main Authors: Yin, Junqing, Gu, Jinyu, Chen, Yongdang, Tang, Wenbin, Zhang, Feng
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial neural networks; Back propagation networks; Deformation; Finite Element Analysis; Finite element method; Impact loads; Load; Mathematical models; Neural networks; Neural Networks, Computer; Plastic deformation; Plastics; Polynomials; Prediction models
• ISSN: 0036-8504; 2047-7163
• 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.