Road Design Layer Detection in Point Cloud Data for Construction Progress Monitoring
JOURNAL OF COMPUTING IN CIVIL ENGINEERING
American Society of Civil Engineers
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Vick, S., & Brilakis, I. (2018). Road Design Layer Detection in Point Cloud Data for Construction Progress Monitoring. JOURNAL OF COMPUTING IN CIVIL ENGINEERING, 32 (5. ARTN 04018029)https://doi.org/10.1061/(ASCE)CP.1943-5487.0000772
Poor performance in transportation construction is well-documented, with an estimated $114.3 billion in global annual cost overrun. Studies aimed at identifying the causes highlighted traditional project management functions like progress monitoring as the most important contributing factors. Current methods for monitoring progress on road construction sites are not accurate, consistent, reliable, or timely enough to enable effective project control decisions. Automating this process can address these inefficiencies. The detection of layered design surfaces in digital as-built data is an essential step in this automation. A number of recent studies, mostly focused on structural building elements, aimed to accomplish similar detection but the methods proposed are either ill-suited for transportation projects or require labelled as-built data that can be costly and time consuming to produce. This paper proposes and experimentally validates a model-guided hierarchical space partitioning data structure for accomplishing this detection in discrete regions of 3D as-built data. The proposed solution achieved an F1 Score of 95.2% on real-world data confirming the suitability of this approach.
Transportation construction, Progress monitoring, Drones, Automation
This research is made possible through funding from the United States Air Force and the Cambridge Commonwealth and International Trust. The authors express gratitude to the Trimble Corporation for their support in lending equipment and expertise to the data collection operation.
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External DOI: https://doi.org/10.1061/(ASCE)CP.1943-5487.0000772
This record's URL: https://www.repository.cam.ac.uk/handle/1810/278061