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February 5, 2026Buildings0 citationsOpen Access

A Refined Method for Inspecting the Verticality of Thin Tower Structures Using the Marching Square Algorithm

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MZMingduan ZhouGWGuanxiu WuYQYan Qin

Key Points

  • The aim is to develop a refined method for assessing the verticality of thin tower structures utilizing the Marching Square algorithm.
  • Utilized ground-based LiDAR to scan and acquire point cloud data of the tower crane.
  • Implemented point cloud registration and denoising to obtain high-precision data.
  • Designed a cross-sectional slicing segmentation strategy for the tower body.
  • Applied the Marching Square algorithm for contour extraction and centroid computation.
  • Executed a least squares method for 3D line fitting to determine the tilt angle and azimuth.
  • Achieved verticality values of 2.45‰, 2.35‰, 2.20‰, and 2.18‰ for different slicing schemes.
  • All values met the maximum verticality requirement of 4‰ as per GB/T 5031-2019.
  • Demonstrated the feasibility and effectiveness of the non-contact inspection method.

Abstract

Conducting regular verticality inspections for thin tower structures is essential for ensuring structural safety, extending service life, and optimizing operation and maintenance strategies. However, the traditional theodolite inspection method, as a commonly used technique for verticality assessment, still has certain limitations, including strict requirements for station setup, the need for high-altitude contact-based operations, and difficulty in accurately resolving the tilt azimuth of the central axis. More importantly, the conventional method provides insufficient understanding of the overall verticality geometric characteristics of thin tower structures, particularly lacking in systematic approaches for characterizing the axis morphology under non-contact, full three-dimensional (3D) perception conditions. Therefore, this study proposes a refined method for inspecting the verticality of thin tower structures using the Marching Square algorithm. The tower body of a tower crane was selected as the experimental subject. Firstly, ground-based LiDAR was employed to scan and acquire the raw point cloud data of the tower crane. After point cloud registration and denoising, high-precision and valid point cloud data of the tower body were obtained. Secondly, a cross-sectional slicing segmentation strategy was designed for the point cloud of the tower body standard sections, and a slice-polygon-contour extraction method based on the Marching Square algorithm was proposed to extract the contour vertices and compute the coordinates of the contour centroids. Finally, a spatial line-fitting algorithm based on the least squares method was proposed to fit a 3D line to the coordinates of the contour centroids, thereby determining the direction vector of the central axis. The direction vector was then subjected to vector operations with the x-axis and z-axis in the station-center space coordinate system to derive the tilt azimuth and tilt angle of the central axis, thereby providing the verticality inspection results of the tower crane. The experimental results indicate that the four cross-section slicing segmentation schemes designed using the proposed method in this study yielded tower crane verticality values of 2.45‰, 2.35‰, 2.20‰, and 2.18‰. All verticality values meet the verticality requirement of no more than 4‰ specified in GB/T 5031-2019 (Tower Cranes). This verifies that the proposed method is feasible and effective, providing a novel, high-precision, and non-contact inspection method for inspecting the anti-overturning stability of thin tower structures.

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Cite This Study

Zhou et al. (2026) studied this question.

synapsesocial.com/papers/6984345ff1d9ada3c1fb2648https://doi.org/10.3390/buildings16030604
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