Damage detection in structural health monitoring using hybrid convolution neural network and recurrent neural network - Dung Bui-Ngoc; Hieu Nguyen-Tran; Lan Nguyen-Ngoc; Hoa Tran-Ngoc; Thanh Bui-Tien; Hung Tran-Viet (Fracture and Structural Integrity)
The process of damage identification in Structural Health Monitoring (SHM) gives us a lot of practical information about the current status of the inspected structure. The target of the process is to detect damage status by processing data collected from sensors, followed by identifying the difference between the damaged and the undamaged states. Different machine learning techniques have been applied to attempt to extract features or knowledge from vibration data, however, they need to learn prior knowledge about the factors affecting the structure. In this paper, a novel method of structural damage detection is proposed using a hybrid convolution neural network and recurrent neural network. A convolution neural network is used to extract deep features while recurrent neural network is trained to learn the long-term historical dependency in time series data. This proposed method which combines two types of features -spatial and temporal- helps to increase discrimination ability when being compared with the one that contains deep features only. Finally, the neural network is applied to categorize the time series into two states - undamaged and damaged. The accuracy of the proposed method was tested on a benchmark dataset of Z24-bridge (Switzerland). The result shows that the hybrid method provides a high level of accuracy in damage identification of the tested structure