Structural damage detection is still a challenging problem owing to the difficulty of extracting damage‐sensitive and noise‐robust features from structure response. This article presents a novel damage detection approach to automatically extract features from low‐level sensor data through deep learning. A deep convolutional neural network is designed to learn features and identify damage locations, leading to an excellent localization accuracy on both noise‐free and noisy data set, in contrast to another detector using wavelet packet component energy as the input feature. Visualization of the features learned by hidden layers in the network is implemented to get a physical insight into how the network works. It is found the learned features evolve with the depth from rough filters to the concept of vibration mode, implying the good performance results from its ability to learn essential characteristics behind the data.

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Ideally, structural health monitoring of civil infrastructure consists of determining, by measured parameters, the location and severity of damage in the structure. Many structural vibration parameters have been used to identify and quantify damage. Using parameters based on structural vibration phase space features for damage detection is a new field in structural health monitoring. In this article, a new parameter based on topology changes of the phase space of vibration signals is proposed to identify structural damage, and an index named changes of phase space topology derived from vibration time history is used to locate the damage. A circular arch structure is used to demonstrate the method. Both numerical simulation and experimental tests of dynamic responses of a scaled arch structure to impact loads are carried out. The obtained structural response data are used to detect structural damage. Both the experimental and numerical results indicate that this method can successfully locate damage. It also demonstrated that this proposed method is more sensitive to damage but less sensitive to noise than modal-based parameters. The proposed damage index can be a good candidate in an online structural health monitoring system, as it depends on global vibration measurements but is more sensitive to structural damage than other global vibration-based parameters such as vibration frequencies and mode shapes.