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The Automated Inspection of Precast Utility Tunnel Segments for Geometric Quality Based on the BIM and LiDAR

Autor(en):









Medium: Fachartikel
Sprache(n): Englisch
Veröffentlicht in: Buildings, , n. 9, v. 14
Seite(n): 2717
DOI: 10.3390/buildings14092717
Abstrakt:

The quality inspection of each precast utility tunnel segment is crucial, especially the cross-sectional dimensions and surface smoothness, since they influence the assembly precision at the construction site. Traditional manual inspection methods are not only time-consuming and costly but also limited in accuracy. In order to achieve a high-precision and high-efficiency geometric quality inspection for multi-type precast utility tunnel segments, this paper proposes an automated inspection method based on the Building Information Model (BIM) and Light Detection and Ranging (LiDAR). Initially, the point cloud data (PCD) of the precast utility tunnel segment are acquired through LiDAR and preprocessed to obtain independent point clouds of the precast utility tunnel segment. Then, the shape of the precast utility tunnel segment is identified using the proposed Cross-Sectional Geometric Ratio Feature Identification (CSGRFI) algorithm. Subsequently, the geometric features of the components are extracted based on preset conditions, and the geometric dimensions are calculated. Finally, the quality inspection results are obtained by comparing with the design information provided by the BIM. The proposed method was validated in a real precast component factory. The results indicate that the method achieved a 100% success rate in identifying the cross-sectional shapes of the segments. Compared with the manual measurement method, the proposed method demonstrated a higher accuracy in the geometric quality assessment and an improved time efficiency by 44%. The proposed method enables the efficient geometric quality inspection of tunnel segments, effectively addressing the construction industry’s need for large-scale, high-quality tunnel projects.

Copyright: © 2024 by the authors; licensee MDPI, Basel, Switzerland.
Lizenz:

Dieses Werk wurde unter der Creative-Commons-Lizenz Namensnennung 4.0 International (CC-BY 4.0) veröffentlicht und darf unter den Lizenzbedinungen vervielfältigt, verbreitet, öffentlich zugänglich gemacht, sowie abgewandelt und bearbeitet werden. Dabei muss der Urheber bzw. Rechteinhaber genannt und die Lizenzbedingungen eingehalten werden.

  • Über diese
    Datenseite
  • Reference-ID
    10795802
  • Veröffentlicht am:
    01.09.2024
  • Geändert am:
    01.09.2024
 
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