A K-Dimensional Tree–Iterative Closest Point Algorithm for Overbreak and Underbreak Assessment of Mountain Tunnels
With the increasing scale of mountain tunnel construction, the control of tunnelling quality is becoming a major concern. The efficient and accurate assessment of overbreak and underbreak is vital to the evaluation and optimization of tunnelling quality, but remains a challenge. Thus, this paper pro...
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2025-01-01
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| author | Zhao Han Xiongyao Xie Genji Tang Peifeng Li Shouren Li |
| author_facet | Zhao Han Xiongyao Xie Genji Tang Peifeng Li Shouren Li |
| author_sort | Zhao Han |
| collection | DOAJ |
| description | With the increasing scale of mountain tunnel construction, the control of tunnelling quality is becoming a major concern. The efficient and accurate assessment of overbreak and underbreak is vital to the evaluation and optimization of tunnelling quality, but remains a challenge. Thus, this paper proposes an assessment method for overbreak and underbreak based on the K-dimensional (KD) tree and Iterative Closest Point (ICP) algorithm. Firstly, point clouds are acquired using laser scanning during tunnelling and 3D modeling is performed. Secondly, the as-designed 3D models are converted into point clouds and registered with the acquired as-built point clouds using the improved ICP algorithm with KD tree searching. Thirdly, through registration, the deviation between the as-designed and as-built point clouds is calculated, providing an assessment of overbreak and underbreak during tunnelling. Finally, the effectiveness of the proposed algorithm is validated by data from an ultra-long mountain tunnel. Compared with other methods, the merits of the proposed method include the following: (a) detailed and comprehensive data can be acquired efficiently and (b) a promising assessment accuracy (over 90%) can be obtained. |
| format | Article |
| id | doaj-art-91f326afec184820a6a7f21b43f650e7 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
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| series | Applied Sciences |
| spelling | doaj-art-91f326afec184820a6a7f21b43f650e72025-01-24T13:19:51ZengMDPI AGApplied Sciences2076-34172025-01-0115256610.3390/app15020566A K-Dimensional Tree–Iterative Closest Point Algorithm for Overbreak and Underbreak Assessment of Mountain TunnelsZhao Han0Xiongyao Xie1Genji Tang2Peifeng Li3Shouren Li4Key Laboratory of Geotechnical and Underground Engineering of the Ministry of Education, Tongji University, Shanghai 200092, ChinaKey Laboratory of Geotechnical and Underground Engineering of the Ministry of Education, Tongji University, Shanghai 200092, ChinaKey Laboratory of Geotechnical and Underground Engineering of the Ministry of Education, Tongji University, Shanghai 200092, ChinaYunnan Linshuang Expressway Co., Ltd., Lincang 677011, ChinaKey Laboratory of Geotechnical and Underground Engineering of the Ministry of Education, Tongji University, Shanghai 200092, ChinaWith the increasing scale of mountain tunnel construction, the control of tunnelling quality is becoming a major concern. The efficient and accurate assessment of overbreak and underbreak is vital to the evaluation and optimization of tunnelling quality, but remains a challenge. Thus, this paper proposes an assessment method for overbreak and underbreak based on the K-dimensional (KD) tree and Iterative Closest Point (ICP) algorithm. Firstly, point clouds are acquired using laser scanning during tunnelling and 3D modeling is performed. Secondly, the as-designed 3D models are converted into point clouds and registered with the acquired as-built point clouds using the improved ICP algorithm with KD tree searching. Thirdly, through registration, the deviation between the as-designed and as-built point clouds is calculated, providing an assessment of overbreak and underbreak during tunnelling. Finally, the effectiveness of the proposed algorithm is validated by data from an ultra-long mountain tunnel. Compared with other methods, the merits of the proposed method include the following: (a) detailed and comprehensive data can be acquired efficiently and (b) a promising assessment accuracy (over 90%) can be obtained.https://www.mdpi.com/2076-3417/15/2/566mountain tunneloverbreak and underbreak3D laser scanningiterative closest point (ICP)K-dimensional (KD) tree |
| spellingShingle | Zhao Han Xiongyao Xie Genji Tang Peifeng Li Shouren Li A K-Dimensional Tree–Iterative Closest Point Algorithm for Overbreak and Underbreak Assessment of Mountain Tunnels Applied Sciences mountain tunnel overbreak and underbreak 3D laser scanning iterative closest point (ICP) K-dimensional (KD) tree |
| title | A K-Dimensional Tree–Iterative Closest Point Algorithm for Overbreak and Underbreak Assessment of Mountain Tunnels |
| title_full | A K-Dimensional Tree–Iterative Closest Point Algorithm for Overbreak and Underbreak Assessment of Mountain Tunnels |
| title_fullStr | A K-Dimensional Tree–Iterative Closest Point Algorithm for Overbreak and Underbreak Assessment of Mountain Tunnels |
| title_full_unstemmed | A K-Dimensional Tree–Iterative Closest Point Algorithm for Overbreak and Underbreak Assessment of Mountain Tunnels |
| title_short | A K-Dimensional Tree–Iterative Closest Point Algorithm for Overbreak and Underbreak Assessment of Mountain Tunnels |
| title_sort | k dimensional tree iterative closest point algorithm for overbreak and underbreak assessment of mountain tunnels |
| topic | mountain tunnel overbreak and underbreak 3D laser scanning iterative closest point (ICP) K-dimensional (KD) tree |
| url | https://www.mdpi.com/2076-3417/15/2/566 |
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