Automation of Inspection Mission Planning Using 4D BIMs and in Support of Unmanned Aerial Vehicle–Based Data Collection
Publication: Journal of Construction Engineering and Management
Volume 147, Issue 3
Abstract
The data collected by unmanned aerial vehicles (UAV) provide unique opportunities for applications, such as construction progress tracking and facility monitoring. To increase the effectiveness of UAV-captured data, inspection mission plans should be designed prior to site visits. The data collection locations must be identified and adjusted based on the user’s objectives. The UAV flight mission must guarantee a visit to each identified location, and the inspection plans must satisfy the constraints imposed by safety requirements, no-fly zones, and UAV’s limited battery life. This problem becomes more complicated indoors due to complex and continuously changing building layouts. This work uses four-dimensional (4D) building information models (BIM) to automatically design optimal UAV mission plans in support of indoor UAV-enabled data acquisition. It automatically identifies the inspection targets based on users’ customizable and multicriteria description of objectives (e.g., columns behind schedule). The three-dimensional (3D) navigable space is automatically calculated by time stamping the 4D models based on the inspection date. The navigable space is further refined based on safety rules. An optimal UAV inspection mission plan is developed using swarm intelligence that ensures complete coverage of targets and minimizes battery use. The method is based on the industry foundation classes (IFC) schema, promoting OpenBIM and interoperability, which are core challenges in the construction information modeling domain.
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Data Availability Statement
Data analyzed during the study were provided by a third party. Requests for data should be directed to the provider indicated in the Acknowledgments. Information about the Journal’s data-sharing policy can be found here: http://ascelibrary.org/doi/10.1061/(ASCE)CO.1943-7862.0001263.
Acknowledgments
This research was financially supported by the Natural Science and Engineering Research Council (Grant No. RGPIN-2017-06792), the Center for Integrated Facility Engineering (CIFE) at Stanford University (Grant No. 2017-06), and the Stanford School of Engineering Fellowship. The data analyzed in this research was provided by the University of Toronto. The authors are grateful to Adrienne De Francesco and Steve Miszuk from the University of Toronto and Perkins+Will, Inc., for their tremendous help.
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Journal of Construction Engineering and Management
Volume 147 • Issue 3 • March 2021
Copyright
© 2020 American Society of Civil Engineers.
History
Received: May 19, 2020
Accepted: Sep 23, 2020
Published online: Dec 23, 2020
Published in print: Mar 1, 2021
Discussion open until: May 23, 2021
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