An improved Alpha-shape algorithm for extracting section contours of the super-high steel bridge tower using point clouds

Abstract

The virtual preassembly of super-high steel bridge towers faces a challenge in the efficient and precise extraction of complex cross-sectional features. Factors such as fabrication errors， gravity-induced deformations， and temperature fluctuations can compromise the accuracy of contour extraction. To address these limitations， an improved Alpha-shape-based point cloud contour extraction method is proposed. The proposed approach uses a hierarchical strategy to process three-dimensional laser scanning point clouds. The processed data are then subjected to curvature-adaptive voxel filtering to reduce acquisition noise. In addition， an enhanced iterative closest point （ICP） variant with correspondence validation accurately aligns the discrete point cloud segments. The proposed curvature-responsive Alpha-shape framework enables multiscale contour delineation through topology-adaptive threshold modulation， which resolves boundary ambiguities in geometrically complex cross-sections. The method was experimentally validated using field-acquired measurement datasets from the Zhangjinggao Yangtze River Bridge tower segments， confirming its capability to reconstruct noncanonical cross-sectional geometries. Three contour extraction methods， including Poisson reconstruction， the conventional Alpha-shape algorithm， and random sample consensus with ICP （RANSAC-ICP）， were compared to evaluate the performance of the proposed Alpha-shape algorithm. The results demonstrate that the proposed method achieves superior contour extraction accuracy and data reduction efficiency， highlighting its effectiveness in contour extraction tasks.

Keywords

super-high steel bridge tower / point cloud / contour extraction / improved Alpha-shape algorithm

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Funding

National Natural Science Foundation of China(52338011)

Start-up Research Fund of Southeast University(RF1028624058)

Southeast University Interdisciplinary Research Program for Young Scholars, the National Key Research and Development Program of China(2024YFC3014103)