Crack detection and analysis method for timber structures of ancient buildings based on laser point cloud data

Cracks in ancient timber structures often exhibit complex morphologies and uneven surfaces, posing significant challenges for traditional crack detection methods that require non-destructive and high-precision capabilities. This study proposes a non-contact crack detection and analysis method based on terrestrial laser scanning point cloud data, integrating Region Growing segmentation, Random Sample Consensus fitting, and Alpha-shapes contour extraction algorithms. The method overcomes the limitations of previous approaches that focus solely on two-dimensional crack features (length and width) by introducing crack depth computation, enabling precise three-dimensional characterization of crack morphology. Experimental results demonstrate that the proposed method effectively addresses the challenges posed by the irregular surfaces and complex crack forms of ancient timber structures, accurately extracting geometric parameters such as crack length, width, and depth, and exhibiting robust performance and adaptability. This research offers a scalable technical approach for the non-destructive health monitoring of ancient timber structures, providing a reliable foundation for conservation measures and safety assessments.