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Damage alarming for bridge expansion joints using novelty detection technique based on long-term monitoring data

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Abstract

Damage alarming and safety evaluation using long-term monitoring data is an area of significant research activity for long-span bridges. In order to extend the research in this field, the damage alarming technique for bridge expansion joints based on long-term monitoring data was developed. The effects of environmental factors on the expansion joint displacement were analyzed. Multiple linear regression models were obtained to describe the correlation between displacements and the dominant environmental factors. The damage alarming index was defined based on the multiple regression models. At last, the X-bar control chart was utilized to detect the abnormal change of the displacements. Analysis results reveal that temperature and traffic condition are the dominant environmental factors to influence the displacement. When the confidence level of X-bar control chart is set to be 0.003, the false-positive indications of damage can be avoided. The damage sensitivity analysis shows that the proper X-bar control chart can detect 0.1 cm damage-induced change of the expansion joint displacement. It is reasonably believed that the proposed technique is robust against false-positive indication of damage and suitable to alarm the possible future damage of the expansion joints.

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Correspondence to Chang-qing Miao  (缪长青).

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Foundation item: Project(2009BAG15B03) supported by the National Science and Technology Ministry of China; Projects(51178100, 51078080) supported by the National Natural Science Foundation of China; Project(BK2011141) supported by the Natural Science Foundation of Jiangsu Province, China; Project(12KB02) supported by the Open Fund of the Key Laboratory for Safety Control of Bridge Engineering (Changsha University of Science and Technology), Ministry of Education, China

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Miao, Cq., Deng, Y., Ding, Yl. et al. Damage alarming for bridge expansion joints using novelty detection technique based on long-term monitoring data. J. Cent. South Univ. 20, 226–235 (2013). https://doi.org/10.1007/s11771-013-1480-4

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  • DOI: https://doi.org/10.1007/s11771-013-1480-4

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