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 combines three key innovations: multi-scale convolutional layers for spatial feature extraction, bidirectional LSTM units for temporal dependency modeling, and an attention mechanism for adaptive sensor data fusion. Evaluated on a dataset from a 21-story reinforced concrete frame under seismic scenarios, the model achieves 97.8% damage detection accuracy, 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, advancing structural health monitoring for proactive maintenance and safety assessments. The findings pave the way for the integration of deep learning techniques in next-generation structural integrity management systems.