Changbin Yan , Weirong Zhao , Tengfei Wang , Ziang Gao , Xiang Xiao , Jianpeng Qin , Yalong Jiang
{"title":"基于岩体切屑图像处理的岩体可钻孔性评价指标及分类体系","authors":"Changbin Yan , Weirong Zhao , Tengfei Wang , Ziang Gao , Xiang Xiao , Jianpeng Qin , Yalong Jiang","doi":"10.1016/j.enggeo.2025.108176","DOIUrl":null,"url":null,"abstract":"<div><div>The boreability of rock masses significantly affects tunnel construction efficiency and cost. To overcome limitations of existing empirical classification systems, this study introduces a quantitative boreability index (<em>BI</em><sub><em>p</em></sub>) based on rock chip characteristics, using new surface theory and image processing techniques including 3D scanning, segmentation, and fractal analysis. <em>BI</em><sub><em>p</em></sub> shows strong correlations with uniaxial compressive strength (<em>UCS</em>) and intactness index (<em>K</em><sub><em>v</em></sub>) (R<sup>2</sup> > 0.84), and aligns well with the field penetration index (<em>FPI</em>), minimizing the influence of non-geological factors. K-means clustering confirms clear associations between <em>BI</em><sub><em>p</em></sub> and cutter head wear rate (<em>ω</em><sub><em>c</em></sub>), enabling the development of a new boreability classification standard and a cutter wear risk warning mechanism. These results offer practical value for improving boreability assessment and equipment protection in shield tunnelling.</div></div>","PeriodicalId":11567,"journal":{"name":"Engineering Geology","volume":"354 ","pages":"Article 108176"},"PeriodicalIF":8.4000,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Evaluation indicators and classification system for rock mass boreability based on rock mass chip image processing\",\"authors\":\"Changbin Yan , Weirong Zhao , Tengfei Wang , Ziang Gao , Xiang Xiao , Jianpeng Qin , Yalong Jiang\",\"doi\":\"10.1016/j.enggeo.2025.108176\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>The boreability of rock masses significantly affects tunnel construction efficiency and cost. To overcome limitations of existing empirical classification systems, this study introduces a quantitative boreability index (<em>BI</em><sub><em>p</em></sub>) based on rock chip characteristics, using new surface theory and image processing techniques including 3D scanning, segmentation, and fractal analysis. <em>BI</em><sub><em>p</em></sub> shows strong correlations with uniaxial compressive strength (<em>UCS</em>) and intactness index (<em>K</em><sub><em>v</em></sub>) (R<sup>2</sup> > 0.84), and aligns well with the field penetration index (<em>FPI</em>), minimizing the influence of non-geological factors. K-means clustering confirms clear associations between <em>BI</em><sub><em>p</em></sub> and cutter head wear rate (<em>ω</em><sub><em>c</em></sub>), enabling the development of a new boreability classification standard and a cutter wear risk warning mechanism. These results offer practical value for improving boreability assessment and equipment protection in shield tunnelling.</div></div>\",\"PeriodicalId\":11567,\"journal\":{\"name\":\"Engineering Geology\",\"volume\":\"354 \",\"pages\":\"Article 108176\"},\"PeriodicalIF\":8.4000,\"publicationDate\":\"2025-06-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Engineering Geology\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0013795225002728\",\"RegionNum\":1,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, GEOLOGICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Engineering Geology","FirstCategoryId":"89","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0013795225002728","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, GEOLOGICAL","Score":null,"Total":0}
Evaluation indicators and classification system for rock mass boreability based on rock mass chip image processing
The boreability of rock masses significantly affects tunnel construction efficiency and cost. To overcome limitations of existing empirical classification systems, this study introduces a quantitative boreability index (BIp) based on rock chip characteristics, using new surface theory and image processing techniques including 3D scanning, segmentation, and fractal analysis. BIp shows strong correlations with uniaxial compressive strength (UCS) and intactness index (Kv) (R2 > 0.84), and aligns well with the field penetration index (FPI), minimizing the influence of non-geological factors. K-means clustering confirms clear associations between BIp and cutter head wear rate (ωc), enabling the development of a new boreability classification standard and a cutter wear risk warning mechanism. These results offer practical value for improving boreability assessment and equipment protection in shield tunnelling.
期刊介绍:
Engineering Geology, an international interdisciplinary journal, serves as a bridge between earth sciences and engineering, focusing on geological and geotechnical engineering. It welcomes studies with relevance to engineering, environmental concerns, and safety, catering to engineering geologists with backgrounds in geology or civil/mining engineering. Topics include applied geomorphology, structural geology, geophysics, geochemistry, environmental geology, hydrogeology, land use planning, natural hazards, remote sensing, soil and rock mechanics, and applied geotechnical engineering. The journal provides a platform for research at the intersection of geology and engineering disciplines.