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Zhang, Hao Xuan

University of British Columbia

Quality Assurance and Quality Control (QA/QC) play a crucial role in the building project life cycle, especially during construction, as discrepancies between as-built structures and as-designed models can lead to cost overruns and schedule delays. Ensuring building quality is of utmost importance, but traditional manual inspections suffer from errors, consume time, and incur significant expenses. This paper describes a deviation detection method for building components using synthetic point clouds and semantic segmen- tation models. The method entails training a three-dimensional semantic segmentation model using synthetic point clouds generated from Building Information Models (BIM) to label each point with an object class, result- ing in a mean intersection over union of 41.1% in semantic segmentation. Subsequently, real point clouds collected onsite are segmented using the same model and then compared with the synthetic point cloud to assess the disparities between the building components of the as-designed and as-built structures. This approach can improve the efficiency of QA/QC by reducing the manual workload of field inspectors.

Text

2024-04-30

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/88121

Civil Engineering

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/