Damage identification using deep learning and long-gauge fiber Bragg grating sensors.

Abstract

This study proposes an innovative structural damage identification method using dynamic response measured by long-gauge fiber Bragg grating (FBG) sensors and accelerometers to train deep convolutional neural networks (DCNNs). At the same time, the pre-trained model is applied to another structure through transfer learning (TL) technology. To verify this method, the I-shaped steel beam vibration test was conducted at first. Three types of data, acceleration, wavelength, and the fusion of the former, are utilized to train convolutional neural network (CNN) models, and the models are then tested and compared. In TL, another CNN is pre-trained using FBG data of steel beam. Next, the data of T-shaped reinforced concrete (RC) beam are employed to train the pre-trained model. The performance of the CNN is evaluated by training history and the confusion matrix. The results show the CNN-based damage identification method can classify the damage pattern accurately. The CNN trained by fusion data has both high classification accuracy and faster training speed. TL technology can greatly reduce the training time of other target tasks in the same fields.