A Multi-Scale CNN-BiLSTM Framework with An Attention Mechanism for Interpretable Structural Damage Detection

Structural damage detection is essential for civil infrastructure safety. The challenges in noise sensitivity, multi-scale feature extraction, and handling bidirectional temporal dependencies are often encountered by traditional methods such as vibration analysis and computer vision. Although potential solutions are offered by recent deep-learning advancements, limitations are frequently imposed by low interpretability and the incapability to adaptively prioritize crucial features within complex time-series data. To address these, a novel hybrid deep-learning framework is proposed. It is integrated with multi-scale convolutional neural networks (CNNs), bidirectional long short-term memory (BiLSTM) networks, and attention mechanisms. Localized time-frequency features are captured from vibration signals by the CNN using multi-scale kernels. Bidirectional temporal dependencies are skillfully captured by the BiLSTM. The interpretability is improved by the attention mechanism through dynamic feature weighting. Experiments on a simulated steel frame demonstrate that detection accuracy and robustness can be enhanced by this framework. This work promotes structural health monitoring, providing a practical tool for engineering applications.