Many research groups in the structural health monitoring community have made efforts to utilize deep learning-based approaches for damage detection on a variety of structures. Among these approaches, structural damage detection through deep convolutional neural networks using raw structural response data has received great attention. However, structural responses are affected not only by structural properties but also by excitation characteristics. For detecting of structures’ damage under seismic excitations, different seismic excitations definitely cause varied structural responses data. In practice, it is impossible to accurately predict the characteristics of future seismic excitation for pre-training the deep convolutional neural network. Therefore, it is essential to investigate the autonomous detection of structural element damage subject to unknown seismic excitation. In this article, a new approach is proposed for detecting structural damage subject to unknown seismic excitation based on a convolutional neural network with wavelet-based transmissibility of structural response data. The transmissibility functions of structural response data are used to eliminate the influence of different seismic excitations. Moreover, contrary to the traditional Fourier transform in the conventional transmissibility function, wavelet-based transmissibility function is presented using the ability in subtle information acquisition of wavelet transform. The wavelet-based transmissibility data of structural responses are used as the inputs to constructed deep convolutional neural networks. Both a numerical simulation example and an experimental test are used to validate the performance of the proposed approach based on deep convolutional neural network.

Structural monitoring provides valuable information on the state of structural health, which is helpful for structural damage detection and structural state assessment. However, when the sensors are exposed to harsh environmental conditions, various anomalies caused by sensor failure or damage lead to abnormalities of the monitoring data. It is inefficient to remove abnormal data by manual elimination because of the massive number of data obtained by monitoring systems. In this paper, a data anomaly detection method based on structural vibration signals and a convolutional neural network (CNN) is proposed, which can automatically identify and eliminate abnormal data. First, the anomaly detection problem is modeled as a time series classification problem. Data preprocessing and data augmentation, including data expansion and down-sampling to construct new samples, are employed to process the original time series. For a small number of samples in the data set, randomly increase outliers, symmetrical flipping, and noise addition methods are used for data expansion, and samples with the same label are added without increasing the original samples. The down-sampling method of symmetrically extracting the maximum value and the minimum value at the same time can effectively reduce the dimensionality of the input sample, while retaining the characteristics of the data to the greatest extent. Using hyperparameter tuning of the classification weights, CNN is more effective in dealing with unbalanced training sets. Finally, the effectiveness of the proposed method is proved by the anomaly detection of acceleration data on a long-span bridge. For the anomaly detection problem modeled as a time series classification problem, the proposed method can effectively identify various abnormal patterns.