A K-Dimensional Tree–Iterative Closest Point Algorithm for Overbreak and Underbreak Assessment of Mountain Tunnels
Source abstract
Abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: 3D Surveying and Cultural Heritage · Tunneling and Rock Mechanics · Geotechnical Engineering and Analysis
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Key Laboratory of Geotechnical and Underground Engineering of the Ministry of Education, Tongji University, Shanghai 200092, China
Department of Geotechnical Engineering, Tongji University, Shanghai 200092, China
State Key Laboratory of Disaster Reduction in Civil Engineering, Tongji University, Shanghai 200092, China
Yunnan Linshuang Expressway Co., Ltd., Lincang 677011, China
Funding
National Key R&D Program of China · National Natural Science Foundation of China · Science and Technology Innovation Plan of Shanghai Science and Technology Commission · State Grid Shanghai Municipal Electric Power Company · Zhejiang Engineering Center of Road and Bridge Intelligent Operation and Maintenance Technology
References (23)
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