A Rapid Identification Technique of Moving Loads Based on MobileNetV2 and Transfer Learning

Rapid and accurate identification of moving load is crucial for bridge operation management early warning overload events. However, it hard to obtain them rapidly via traditional machine learning methods, due their massive model parameters complex network structure. To this end, paper proposes a novel method perform loads using MobileNetV2 transfer learning. Specifically, the dynamic responses vehicle–bridge interaction system are firstly transformed into two-dimensional time-frequency image by continuous wavelet transform construct database. Secondly, pre-trained based on ImageNet transferred task strategy describing mapping relationship between structural response these specified loads. Then, can be performed through inputting established relationship. Finally, effectiveness verified numerical simulation. The results show that accurately identify vehicle weight, speed information, presents excellent strong robustness. In addition, has faster requires less computational resources than several deep convolutional neural models in identification, which provide idea rapid