Research on the Application of Deep Learning Algorithm in the Damage Detection of Steel Structures

This study introduces a novel Convolutional Long Short-Term Memory (ConvLSTM) network for steel structure damage detection, aimed at enhancing the accuracy and reliability of structural health monitoring systems. The model's core innovations include: 1) the incorporation of multi-scale convolutional layers to effectively capture spatial features at various scales; 2) the design of bidirectional LSTM units to enhance modeling of long-term temporal dependencies; and 3) the implementation of an attention mechanism for adaptive fusion of multi-source sensor data. The model's performance was evaluated using a comprehensive dataset derived from a 21-story reinforced concrete frame structure, subjected to various seismic scenarios. Results demonstrate the superior performance of the ConvLSTM model, achieving a 97.8% damage detection accuracy, significantly outperforming traditional methods and other deep learning architectures. The model's efficacy was further validated through real-world applications on diverse structures, including a 30-story office building, a cable-stayed bridge, and an offshore oil platform. Transfer learning strategies were successfully implemented to adapt the model to different structural contexts, addressing the challenge of limited labeled data. The proposed approach exhibited remarkable robustness and long-term stability across various environmental conditions and loading scenarios. This research contributes to the advancement of structural health monitoring practices, offering improved damage detection capabilities with potential applications in proactive maintenance strategies and enhanced structural safety assessments. The findings pave the way for the integration of deep learning techniques in next-generation structural integrity management systems.