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11 August 2020 A Study on Non-baseline Damage Identification for Sea Crossing Bridge Beam Structure
Fei Ye, Jiaquan Wu, Kun Ma
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Abstract

Ye, F.; Wu, J.-Q., and Ma, K., 2020. A study on non-baseline damage identification for sea crossing bridge beam structure. In: Qiu, Y.; Zhu, H., and Fang, X. (eds.), Current Advancements in Marine and Coastal Research for Technological and Sociological Applications. Journal of Coastal Research, Special Issue No. 107, pp. 41-44. Coconut Creek (Florida), ISSN 0749-0208.

The sea crossing bridge will be damaged by various natural and human factors, which will reduce the service performance and safety reliability of the bridge. With the passage of time, the damage of the cross sea bridge will gradually accumulate, which will cause major safety accidents. Therefore, we need to identify the damage location and damage degree of the bridge, which will improve the real-time health detection of the bridge. In view of the common damage of the bearing of continuous beam bridge which is easy to be damaged, this paper uses statistical moment to identify the damage. In this paper, the different damage conditions of simply supported beam and continuous beam are identified through the identification index of strain statistical moment, which can measure the damage location and degree. Then, this paper analyzes the damage identification under the influence of no noise and 15% noise respectively, which will improve the damage identification technology of beam structure. Through the dynamic characteristics of the beam bridge, this paper carries out the damage identification model test of the beam bridge structure.

©Coastal Education and Research Foundation, Inc. 2020
Fei Ye, Jiaquan Wu, and Kun Ma "A Study on Non-baseline Damage Identification for Sea Crossing Bridge Beam Structure," Journal of Coastal Research 107(sp1), 41-44, (11 August 2020). https://doi.org/10.2112/JCR-SI107-011.1
Received: 19 May 2020; Accepted: 16 June 2020; Published: 11 August 2020
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KEYWORDS
beam structure
cross sea bridge
damage identification
Strain statistical moment
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