Prediction of Nuclear Explosion Vibration and MRD Semi‐Active Control Based on Deep Learning

This research centers on the application of deep learning in predicting nuclear explosion vibration loads and the semi‐active control of magnetorheological dampers (MRDs). It delves deeply into the performance of the Convolutional Neural Network (CNN) and the long short‐term memory (LSTM) network. The time history of impact acceleration in the near and far zones of a nuclear explosion was simulated, and the prediction performances of the two models under different ratios of the training set to the test set were compared. The results indicate that the LSTM has a faster training speed and performs better in most training set error indices. In terms of the test set, the two models have their respective advantages and disadvantages under different ratios. Regarding the prediction of the semi‐active control effect of MRDs, a corresponding system was constructed, and calculations and analyses were carried out. It was found that the LSTM has high training efficiency and generally better performance on the training set. On the test set, the LSTM performs slightly better when the ratio is 0.8:0.2, while the CNN performs better when the ratio is 0.5:0.5. This study provides a theoretical basis and technical support for nuclear explosion vibration prediction and the improvement of the blast resistance of protective structures. It also points out that future research can further optimize the model structure and parameters and explore more effective data processing methods to expand the application of deep learning models in the fields of military and protective engineering.