Analysis of Structural Damage Identification Technology Based on the Bridge Truss

To solve the problem of accuracy and reliability of damage identification of old bridge structures, this paper compares and analyzes three damage identification methods: Bayesian update, curvature modal analysis and BP neural network. The performance comparison is carried out from the four dimensions of local damage identification accuracy, data demand and utilization efficiency, anti-interference and uncertainty quantification, and then the reliability is analyzed from the essential difference between the deterministic and uncertain methods. The analysis shows that the Bayesian updating method has more advantages in uncertainty quantification and anti-interference. The physical mechanism of curvature modal analysis is clear, which is suitable for damage location under the condition of low noise and high-density measurement points; the BP neural network relies on large-scale samples, and its generalization ability is limited. Finally, I propose that the future should focus on the research direction of "physical mechanism+data-driven" hybrid model construction, lightweight real-time monitoring technology development, and deep integration of deep learning and uncertainty quantification.