Tilt Measurement Method of Wooden Columns in Traditional Timber Buildings Based on Adaptive RANSAC and PCA Method
Minyan Zhan1, Wei Yang2,3, Minghao Wu4,*, Hsin-Yi Wang5, Yu-Hsien Ho5
1 Urban and Rural Architecture School, Minxi Vocational & Technical College, Longyan, China
2 Fujian Academy of Building Research Co., Ltd., Fuzhou, China
3 Fujian Provincial Key Laboratory of Green Building Technology, Fuzhou, China
4 College of Civil Engineering, Fujian University of Technology, Fuzhou, China
5 Department of Civil Engineering and Geomatics, Cheng Shiu University, Kaohsiung, Taiwan
* Corresponding Author: Minghao Wu. Email: 
(This article belongs to the Special Issue: Advanced Detection Technologies and Interpretable Machine Learning Methods in Energy Infrastructure)
Structural Durability & Health Monitoring https://doi.org/10.32604/sdhm.2026.077926
Received 19 December 2025; Accepted 13 February 2026; Published online 16 March 2026
Abstract
The inclination of wooden columns is a key indicator for evaluating the structural safety of traditional timber buildings in China. However, accurate measurement is challenging because these columns typically exhibit natural tapering, with diameters decreasing from the base to the top, and surface irregularities such as artificial cuts, cracks, and knots. Both the intrinsic geometric characteristics and surface defects reduce the precision of coordinate acquisition and the reliability of inclination estimation. To overcome these limitations, this study proposes a novel inclination measurement method for wooden columns in traditional timber buildings based on multi-section measurement and spatial line fitting. An adaptive random sample consensus algorithm is employed to effectively remove outliers induced by surface damage and measurement noise. Subsequently, principal component analysis is used to fit a three-dimensional spatial line through the center points of multiple cross-sections, enabling accurate computation of the inclination angle while accounting for structural imperfections. Moreover, the effects of damage defects, the number of measuring points, the measurement range, and the number of selected cross-sections on measurement accuracy are systematically investigated through statistical analysis, and corresponding recommended values for engineering practice are provided. The proposed method offers an efficient and reliable solution for inclination measurement, supporting the inspection and structural safety assessment of traditional timber columns.
Keywords
Traditional timber building; inclination measurement of wooden column; adaptive random sample consensus algorithm; principal component analysis
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