Machine-learning to see defects: a hybrid attention-ConvLSTM-based convolutional neural network deep learning architecture for structural damage detection

Abstract

Vibration measurement serves as the basis for structural damage detection. To detect damage, vibration measurement and frequency estimation through image sequence analysis continue to receive increasing attention. In this work, we demonstrate that structural damage prediction can be achieved using a deep learning neural network architecture. In this paper, we seek to learn and see the structural damage directly from videos using deep convolutional neural networks (CNN). The key idea is to use each pixel of an image taken from a digital camera, extracting the spatiotemporal information, like a sensor to capture the modal frequencies of a vibrating structure. We develop attention-based architecture to detect subtle signals from a specific source to visualize high resolution of dynamic properties of the structures to infer existing structural damage. We first extract the high discriminative features of video frames using the CNN. Then we leverage conv-long short-term memory (ConvLSTM) with the extracted features as inputs to capture the temporal dynamics in videos. The attention mechanisms are embedded in the network to ensure the model learns to focus selectively on the dynamic frames across the video clips. Our computer vision-based deep learning model takes the video of a vibrating structure as input and outputs about the health of the structure. We demonstrate, using reliable empirical results, the proposed model is efficient, autonomous, and accurate. The proposed method is verified using a few laboratory experiments. Our experimental results demonstrate that the proposed method can achieve acceptable prediction accuracy even.