Prediction of Ultimate Bearing Capacity and Structural Optimization of Aluminum Alloy Plate Joints Based on Artificial Neural Network

• Published in: International journal of steel structures 2021, 21(5), , pp.1759-1774
• Main Authors: Zhong, Chang-jun, Feng, Ruo-qiang, Zhang, Zhi-jie
• Format: Article
• Language: English
• Subjects: Accuracy; Aluminum alloys; Aluminum base alloys; Artificial neural networks; Bearing capacity; Civil Engineering; Correlation coefficients; Diameters; Engineering; Finite element method; Materials Science; Mathematical models; Metal plates; Neural networks; Optimization; Parameters; Solid Mechanics; Thickness; 토목공학
• ISSN: 1598-2351; 2093-6311
• DOI: 10.1007/s13296-021-00533-7

An artificial neural network model was established to predict the ultimate bearing capacity of aluminum alloy plate joints under compression-bending state. The input parameters of the network are composed of bolt diameter (R), flange plate thickness (T) and connecting plate thickness (L). The output parameter is ultimate bearing capacity. In this paper, firstly the finite element calculation results are compared with the experimental results to verify the accuracy of finite element simulation. Then 216 sets of data were obtained using the finite element program ABAQUS, which were used for training, verification and testing of the neural network model. In addition, the influence of sample size on the prediction accuracy of neural network is analyzed, and a structural optimization method combining finite element calculation and neural network prediction is proposed. The results show that the neural network model is accurate and effective in predicting ultimate bearing capacity, and the linear regression correlation coefficient is 0.98438. The data sample can be reduced to a certain extent to save the time costs and computing resources; It is reasonable and efficient to use the method of finite element parameter analysis and artificial neural network prediction to optimize the structure, which broadens the thinking of structure optimization.