Abstract
With the large-scale construction of high-speed railway bridges, monitoring their settlement has become an increasingly important task to ensure the safe operation of a high-speed railway. This paper presents a case study of a 100-km-long portion of an intelligent settlement monitoring system for the high-speed railway bridges (SMAIS) of the Bei**g–Shanghai railway. The SMAIS consists of a sensor subsystem (SS), data acquisition and transmission subsystem (DAQ), data processing and analysis subsystem (DPA), database subsystem (DB), and data display and early warning subsystem (DEW). The SMAIS has the advantages of modularisation, low coupling, and interface standardisation. The general design principles of the SMAIS were studied, including its monitoring range calculation, monitoring point positions, and early warning threshold value determination. Proposals are made for the design of the SMAIS architecture and functions of its subsystems. The installation process and standard techniques for the SMAIS are also presented. The sensor precision test, temperature stability test, and general monitoring results are presented. An advanced continuous median data filtering-algorithm that considers the vehicle influence and error transmission is proposed.
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References
Li H, Ou J (2006) Design and implementation of health monitoring systems for cable-stayed bridges (I): design methods. China Civ Eng J 39(4):39–44 (in Chinese)
Zhuo Y, Wang X, Zhang J (2015) Development and application of automatic monitoring system SMAIS for settlement of high-speed railway. J Railway Eng Soc 4(199):10–15 (in Chinese)
Liu Z, Li N, Feng L et al (2010) Design and implementation of structural monitoring systems for **houmen Bridge (I): design methods. Eng Sci 12(7):96–100 (in Chinese)
Zhuo Y, Wang F (2011) Research on intelligent diagnosis methods of a hybrid girder cable–stayed bridge based on artificial neural networks. J Railway Eng Soc 12(159):57–63 (in Chinese)
Ou J (2003). Some recent advances of intelligent health monitoring systems for civil infrastructures in mainland China. In: Proceedings of the first international conference on structural health monitoring and intelligent infrastructure, Tokyo, Japan, pp 131–144
Feng L, Li N, Zhang G et al (2009) Status Quo of development and trend of safety monitoring system for long span bridge structure in China. Highway 5:176–181 (in Chinese)
Ou J (2004). Practical implementations of intelligent health monitoring systems in HIT. In: Proceedings of North American Euro Pacific workshop for sensing issues in civil structural health monitoring. Hawaii, USA
Chen SS, Kim S (1995) Automated signal monitoring using neural networks in a smart structural system. J Intell Mater Syst Struct 6:508–515
Chang SP, Kim S et al (2008) Health monitoring system of a self-anchored suspension bridge (planning, design and installation/operation). Struct Infrastruct Eng 4(3):193–205
Liu Z, Li N, Guo J et al (2010) Design and implementation of structural monitoring systems for **houmen Bridge (II): implementations. Eng Sci 12(7):101–106
Wang X (2017) Research of automatic settlement monitoring system for long trunk high speed railway. High Speed Railway Technol 8(6):68–73
Song W (2016) A filtering method of automatic monitoring data of settling of high speed railway based on median principle. Railway Investig Surv 2:9–11 (in Chinese)
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Yi, Z., **shen, G. & Xu, W. Intelligent settlement monitoring system of high-speed railway bridge. J Civil Struct Health Monit 9, 307–323 (2019). https://doi.org/10.1007/s13349-019-00334-x
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DOI: https://doi.org/10.1007/s13349-019-00334-x