| contributor author | Li, Xinping | |
| contributor author | Cui, Haojie | |
| contributor author | Wang, Liangjun | |
| contributor author | Qu, Dengxing | |
| contributor author | Du, Peng | |
| contributor author | Dai, Aoxiong | |
| contributor author | Shang, Zichen | |
| date accessioned | 2026-08-20T21:28:35Z | |
| date available | 2026-08-20T21:28:35Z | |
| date copyright | 2026/05/17 | |
| date issued | 2026 | |
| identifier other | JCCEE5.CPENG-7391.pdf | |
| identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4314517 | |
| description abstract | AbstractAccurate quantification of overbreak and underbreak is essential for tunnel quality,
cost, and stability. Point-cloud methods struggle with sparse labels, heavy site noise,
and strict profile continuity. We introduce a three-dimensional (3D)–... | |
| publisher | American Society of Civil Engineers | |
| title | Deep Learning–Based Few-Shot Method for Detection and Evaluation of Tunnel Overbreak and Underbreak | |
| type | Journal Article | |
| journal volume | 40 | |
| journal issue | 5 | |
| journal title | Journal of Computing in Civil Engineering | |
| identifier doi | 10.1061/JCCEE5.CPENG-7391 | |
| journal fristpage | 04026050-1 | |
| journal lastpage | 04026050-12 | |
| page | 12 | |
| tree | Journal of Computing in Civil Engineering:;2026:;Volume ( 040 ):;issue: 005 | |
| contenttype | Fulltext | |
