CA mortar void identification for ballastless track using 1D-CNN
In order to solve the challenge of identifying the location of void in Cement-emulsified Asphalt mortar(CA mortar),a modeling and calculation scheme was proposed for ballastless tracks considering the CA mortar void.Based on the CRTS I slab ballastless track beam-shell modeling theory,a non-linear spring was used to simulate CA mortar void damage,and the fastener forces were input as the excitation of the model to obtain the vertical acceleration dataset at the monitoring points.Second,data augmentation was performed by scaling the sequences and adding noise to enhance the differences in the dataset.These approaches aimed to improve the recognition model to obtain more accurate recognition results.Finally,a method for the identification of voids was established.A one-dimensional Convolutional Neural Network(1D-CNN)was built to identify the location of CA mortar voids.Evaluation metrics such as accuracy,recall,and precision were constructed to assess the identification results and visualize the results using the t-SNE dimensionality reduction algorithm.The advantages of 1D-CNN applied to CA mortar void recognition were discussed by comparing the performance of the other 3 neural networks.The results show that the beam-shell model reflects the mechanical intricacies of the ballastless track and is feasible for the simulation of the CA mortar void problem.Data augmentation by scaling the dataset signal and adding noise can improve the performance of the deep learning model.From a deep learning perspective,the CA mortar void in the middle of the slab is less damaging and should focus on maintaining the CA mortar void at the slab end.1D-CNN has a shorter running time compared with the other 3 models and has a greater advantage in the CA mortar void location identification dataset,with an identification accuracy of more than 95%.The results of the study provide a reference for further improving the automation and intelligence of ballastless track structure monitoring.
ballastless trackfinite element modelCA mortardeep learningvoid identification
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