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Structural steel products are created within a structural mill. The process begins with a beam blank that is reheated and then fed through a series of rollers. These rollers progressively shape the metal into its final form. Adjusting the spacings between the rollers allows for the production of different sections with the same nominal dimensions.
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Geometric Monitoring of Steel Structures Using Terrestrial Laser Scanning and Deep Learning.

João Ventura1, Jorge Magalhães1, Tomás Jorge1

  • 1iBuilt, School of Engineering, Polytechnic of Porto, 4249-015 Porto, Portugal.

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Summary
This summary is machine-generated.

This study introduces an automated method using laser scanning and AI to detect geometric deviations in steel buildings, ensuring quality control and compliance with EN 1090-2:2020 standards.

Keywords:
EN 1090-2: 2020YOLOv8point cloudsteel structuresstructural geometric monitoringterrestrial laser scanning

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Area of Science:

  • Structural Engineering
  • Computer Vision
  • Metrology

Background:

  • Geometric precision is critical for industrial steel building quality and stability, adhering to EN 1090-2:2020 standards.
  • Manual geometric verification is labor-intensive and prone to errors.

Purpose of the Study:

  • To develop an automated methodology for detecting geometric deviations in steel structures.
  • To compare as-built structures with design models using Terrestrial Laser Scanning (TLS) and AI.

Main Methods:

  • A pipeline processing 3D point clouds from TLS by projecting them into 2D images.
  • Training a YOLOv8 segmentation model for steel cross-section detection, classification, and segmentation.
  • Applying synthetic data augmentation for improved model generalization.
  • Quantifying positional and angular displacements using Oriented Bounding Boxes.

Main Results:

  • Achieved 70.20% mAp@50-95 in segmentation metrics with data augmentation.
  • Successfully assessed up to 94% of structural elements in field applications.
  • Obtained 97% valid segmentations for reliable geometric verification.

Conclusions:

  • The proposed methodology effectively detects geometric deviations in industrial steel buildings.
  • Automated detection ensures compliance with assembly standards and enhances structural integrity.
  • The approach is robust, handling incomplete or occluded geometries effectively.