Structural Performance Monitoring Using a Dynamic Data-Driven BIM Environment
Publication: Journal of Computing in Civil Engineering
Volume 32, Issue 3
Abstract
Structural health monitoring data has not been fully leveraged to support asset management due to a lack of effective integration with other data sets. A building information modeling (BIM) approach is presented to leverage structural monitoring data in a dynamic manner. The approach allows for the automatic generation of parametric BIM models of structural monitoring systems that include time-series sensor data, and it enables data-driven and dynamic visualization in an interactive 3D environment. The approach supports dynamic visualization of key structural performance parameters, allows for the seamless updating and long-term management of data, and facilitates data exchange by generating models compliant with industry foundation classes (IFC). A newly constructed bridge near Stafford, United Kingdom, with an integrated fiber-optic sensor–based monitoring system was used to test the capabilities of the developed approach. The case study demonstrated how the developed approach facilitates more intuitive data interpretation; provides a user-friendly interface to communicate with various stakeholders; allows for the identification of malfunctioning sensors, thus contributing to the assessment of monitoring system durability; and forms the basis for a powerful data-driven asset management tool. In addition, this project highlights the potential benefits of investing in the development of data-driven and dynamic BIM environments.
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Acknowledgments
The authors would like to acknowledge the EPSRC and Innovate U.K. for funding this research through the Centre for Smart Infrastructure and Construction (CSIC) Innovation and Knowledge Centre. This research was carried out under EPSRC Grant No. EP/L010917/1. Data related to this publication are available at the University of Cambridge data repository (https://doi.org/10.17863/CAM.13630).
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Journal of Computing in Civil Engineering
Volume 32 • Issue 3 • May 2018
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©2018 American Society of Civil Engineers.
History
Received: Jul 17, 2017
Accepted: Oct 3, 2017
Published online: Feb 9, 2018
Published in print: May 1, 2018
Discussion open until: Jul 9, 2018
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