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Levee Safety Monitoring: Algorithm for Feature Recognition in Point Clouds of Levee Landslides
Seepage failure of levees can cause landslides and other hazards. Three-dimensional (3D) laser-scanning technology has become a new method for collecting levee hazard data. In this study, the 3D characteristics of landslide point clouds were investigated through systematic indoor model tests, and a feature recognition algorithm applicable to levee landslides was proposed. The major outcomes of this study are as follows: 1) the formulation of an adaptive random sampling boundary extraction (A-R-B) algorithm, which integrates random sample consensus plane segmentation, adaptive distance threshold calculation, and boundary extraction for levee landslide disaster recognition; 2) through feasibility analyses and accuracy tests of the A-R-B algorithm, this study demonstrated the capacity of the proposed method to accurately recognise the features of levee landslides, with a relative accuracy of 1 cm and an absolute accuracy of 3.5 cm in the extraction process; 3) the testing of the A-R-B algorithm and optimal parameters for the recognised levee landslide features using the point clouds obtained from laboratory models.
Levee Safety Monitoring: Algorithm for Feature Recognition in Point Clouds of Levee Landslides
Seepage failure of levees can cause landslides and other hazards. Three-dimensional (3D) laser-scanning technology has become a new method for collecting levee hazard data. In this study, the 3D characteristics of landslide point clouds were investigated through systematic indoor model tests, and a feature recognition algorithm applicable to levee landslides was proposed. The major outcomes of this study are as follows: 1) the formulation of an adaptive random sampling boundary extraction (A-R-B) algorithm, which integrates random sample consensus plane segmentation, adaptive distance threshold calculation, and boundary extraction for levee landslide disaster recognition; 2) through feasibility analyses and accuracy tests of the A-R-B algorithm, this study demonstrated the capacity of the proposed method to accurately recognise the features of levee landslides, with a relative accuracy of 1 cm and an absolute accuracy of 3.5 cm in the extraction process; 3) the testing of the A-R-B algorithm and optimal parameters for the recognised levee landslide features using the point clouds obtained from laboratory models.
Levee Safety Monitoring: Algorithm for Feature Recognition in Point Clouds of Levee Landslides
KSCE J Civ Eng
Liu, Jian (author) / Zhou, Lizhi (author) / Li, Zhanhua (author) / Cui, Lizhuang (author) / Cheng, Sen (author) / Zhao, Hongbing (author) / Luo, Hongzheng (author) / Qi, Minmin (author) / Xie, Quanyi (author)
KSCE Journal of Civil Engineering ; 28 ; 4396-4407
2024-10-01
12 pages
Article (Journal)
Electronic Resource
English
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