A parallel neural network based structural anomaly detection: Leveraging time-frequency domain features

Structural anomaly detection is essential for ensuring the safety, reliability, and longevity of building engineering. By identifying deviations from normal patterns, it enables early intervention and prevents potential failures. However, most existing methods rely on a single feature extracted from the time domain or the frequency domain. Time-domain features alone are insufficient to capture variations in the frequency components, while frequency-domain features fail to account for transient behaviours in the time domain. This limitation significantly reduces detection performance, particularly when dealing with nonlinear and non-stationary signals. To address the issues, this study proposes a new framework for anomaly detection using parallel time convolutional networks (TCN) and wavelet decomposition based convolutional neural networks (WD-CNN), termed PTWC. In this framework, time-frequency domain features are achieved by utilizing TCN to capture time-domain features and WD-CNN to capture frequency-domain features, distinguishing structural anomaly patterns. The proposed PTWC framework is validated on an actual three-story frame structure. Compared with ten baseline methods, the experimental results demonstrate that PTWC has high accuracy and achieves at least 7 % improvement in area under the curve (AUC) scores, thus confirming its superior performance in structural anomaly detection.