In this paper, an innovative two-level damage detection method applicable to real-world online structural health monitoring (SHM) systems is proposed for in-service large steel arch bridges. The method consists of Level 1 damage detection practice that includes strain data acquisition and damage location using the damage index based on the fractal theory, and Level 2 damage detection practice that includes acceleration sample acquisitions and dynamic model updating to quantify the damage. A numerical case study of the Yingzhou bridge based on various damage cases demonstrated the effectiveness of the proposed damage detection method. It is revealed that Level 1 damage detection is sufficiently robust against the standard measurement noise and normal temperature variations. The study results also indicated that the accuracy of Level 2 damage detection largely depends on whether the initial structure imperfections are taken into account, and whether the utilized model updating method is effective under model errors.

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