Abstract
Structural health monitoring is an important task in the construction and maintenance stage. Because sensors are widely distributed in large and complex building space, it is difficult to visualize and locate hazard sources. In this paper, a visual warning framework of structural health monitoring is proposed based on the building information modeling (BIM) platform. The monitoring data bound with the sensors is stored in the database, and the monitoring data is mined to obtain the warning information by the deep learning algorithm long short-term memory (LSTM); The BIM elements are associated with sensors to make the monitoring area correspond to the BIM model so that the sensor and monitoring area can be visible on the BIM platform. Through the development of an integrated plug-in, the user interface and the database are connected by back-end controls to realize the three-dimensional display of the monitoring space on the BIM platform, corresponding with the visualization of the monitoring data and the automatic warning based on LSTM. This study also integrates the Internet of things technology to automatically control the indicator lights based on real-time sensor data and prediction data. The system framework integrates the application of BIM, deep learning algorithm and Internet of things in structural health monitoring, it realizes the spatial visualization of monitoring area corresponding with monitoring data information, quickly locates and issues early warning of dangerous components. It provides a basis for remote structural health monitoring and safety management.
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Acknowledgments
The authors are grateful for the support provided by the Co-funding of the National Natural Science Foundation of China and Shenhua Group Corporation Ltd (Grant No.U1261212) and the Program of Major Achievements Transformation and Industrialization of Beijing Education Commission (Grant No. ZDZH20141141301).
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Hou, G., Li, L., Xu, Z. et al. A BIM-Based Visual Warning Management System for Structural Health Monitoring Integrated with LSTM Network. KSCE J Civ Eng 25, 2779–2793 (2021). https://doi.org/10.1007/s12205-021-0565-0
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DOI: https://doi.org/10.1007/s12205-021-0565-0