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High-Rise Building Construction Progress Measurement from Top View Based on Component Detection

Author(s): ORCID

Medium: journal article
Language(s): English
Published in: Buildings, , n. 2, v. 12
Page(s): 106
DOI: 10.3390/buildings12020106
Abstract:

The construction progress of a high-rise building is hidden by clutter such as formwork, wood slats, and rebar, making it difficult to measure its progress through existing automated techniques. In this paper, we propose a method to monitor the construction process of high-rise buildings. Specifically, by using the target detection technique, unfinished building components are identified from the top view, and then the identified components are registered to the BIM elements one by one. This is achieved by comparing the position relationship between the target detection results and the projection area of the BIM elements on the imaging plane. Finally, the overall construction progress is inferred by calculating the number of identified and registered components. The method was tested on a high-rise building construction site. The experimental results show that the method is promising and is expected to provide a solid basis for the successful automatic acquisition of the construction process. The use of top view reduces occlusion compared to similar methods, and the identification of the unfinished component makes the method more suitable for the actual construction sites of high-rise buildings. In addition, the combination of target detection and rough registration allows this method to take full advantage of the contextual information in the images and avoid errors caused by misidentification.

Copyright: © 2022 by the authors; licensee MDPI, Basel, Switzerland.
License:

This creative work has been published under the Creative Commons Attribution 4.0 International (CC-BY 4.0) license which allows copying, and redistribution as well as adaptation of the original work provided appropriate credit is given to the original author and the conditions of the license are met.

  • About this
    data sheet
  • Reference-ID
    10657779
  • Published on:
    17/02/2022
  • Last updated on:
    01/06/2022
 
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