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An integrated system for building structural health monitoring and early warning based on an Internet of things approach

Author

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  • Jun Wang
  • Yongfeng Fu
  • Xiaokang Yang

Abstract

The intelligent security monitoring of buildings and their surroundings has become increasingly crucial as the number of high-rise buildings increases. Building structural health monitoring and early warning technology are key components of building safety, the implementation of which remains challenging, and the Internet of things approach provides a new technical measure for addressing this challenge. This article presents a novel integrated information system that combines Internet of things, building information management, early warning system, and cloud services. Specifically, the system involves an intelligent data box with enhanced connectivity and exchangeability for accessing and integrating the data obtained from distributed heterogeneous sensing devices. An extensible markup language (XML)–based uniform data parsing model is proposed to abstract the various message formats of heterogeneous devices to ensure data integration. The proposed Internet of things–based integrated information system structure was applied for monitoring an actual pit excavation engineering site. Three early warning levels were implemented according to rules based on the threshold value, which determined the specific safety personnel to be notified. The proposed Internet of things–based integrated information system is demonstrated to improve the effectiveness of monitoring processes and decision making in construction informatics applications. Our work highlights the crucial importance of a systematic approach toward integrated information systems for effective information collection and structural health monitoring.

Suggested Citation

  • Jun Wang & Yongfeng Fu & Xiaokang Yang, 2017. "An integrated system for building structural health monitoring and early warning based on an Internet of things approach," International Journal of Distributed Sensor Networks, , vol. 13(1), pages 15501477166, January.
  • Handle: RePEc:sae:intdis:v:13:y:2017:i:1:p:1550147716689101
    DOI: 10.1177/1550147716689101
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