Transfer learning-based crashworthiness prediction for the composite structure of a subway vehicle

Abstract

Due to the lack of load/displacement sensors in a complex and uncertain crash test/accident of rail vehicles (e.g., vehicle-to-vehicle or train-to-train collision), only structural deformation images can be obtained while the crashworthiness indicators (e.g., force, displacement, energy absorption) cannot be measured directly. This paper aims to propose a transfer learning-based inverse method for extracting the structural parameters and crashworthiness characteristics by the deformed pictures of energy-absorbing structures. A finite element model of an energy-absorbing structure was firstly established and calibrated by experiments. Then, a number of deformation images were captured from the numerical design of experiment (DOE) through coding languages, which were saved as TFRecord format to reduce the computational time during the training of transfer learning models (i.e., VGG16, LetNet, AlexNet and ResNet50). The result showed that the transfer learning model, ResNet50, exhibited the best performance with R2 of 0.736 and 0.981, respectively, for predicting the structural parameters and crashworthiness characteristics. In addition, the number of full connection layers should be reasonably selected on the premise of maintaining accuracy and efficiency. A group of deformation pictures were randomly used as samples to validate the prediction of structural parameters and crashworthiness through the trained transfer learning model, where good consistence was observed. The proposed method is expected to bring the image recognition and big data prediction into the design and test of composite energy-absorbing structures, thus, auxiliary improve the crashworthiness of rail vehicles.