| 隧道掌子面岩体裂隙快速识别方法 |
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| 引用本文： | 冷彪,张毅,杨辉,侯高鹏.隧道掌子面岩体裂隙快速识别方法[J].西南交通大学学报,2021,56(2):246-252, 322. |
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| 作者姓名： | 冷彪 张毅 杨辉 侯高鹏 |
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| 基金项目： | 国家自然科学基金（51108386，51478394）；中央高校基本科研业务费专项资金科技创新项目（2682015CX096） |
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| 摘 要： | 隧道掌子面上含有许多地质信息，若能充分提取和分析，将有助于对隧道工程地质状态作出评价，用于指导隧道设计和施工. 以隧道掌子面数码图像为基础，对掌子面上岩体裂隙检测、提取、分组算法进行研究. 首先，基于数字图像处理技术对掌子面岩体裂隙目标分割算法进行分析，根据分割结果，通过图像细化和边界线拟和、分离、合并、过滤，连接不连续边界，过滤短边界，形成较完整的岩体边界线识别结果；然后，计算岩体边界线视倾角，将视倾角相近的边界线合并为一组，实现对裂隙边界线的自动分组；最后，将本方法应用于实际掌子面岩体图像测试其有效性. 测试结果表明:该方法基本实现了对掌子面上岩体裂隙的自动提取和分组；对具有明显裂隙的掌子面岩体，本算法能较完整的提取出岩体裂隙，错误提取率不超过10%，并实现了自动分组，自动分组错误率不超过5%，提高了掌子面岩体分析的自动化程度，可用于地质素描，并为掌子面围岩分级提供参考依据.
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| 关 键 词： | 隧道掌子面 图像处理 岩体裂隙识别 |
| 收稿时间： | 2019-07-30 |
Rapid Recognition of Rock Mass Fractures in Tunnel Faces |
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| LENG Biao,ZHANG Yi,YANG Hui,HOU Gaopeng.Rapid Recognition of Rock Mass Fractures in Tunnel Faces[J].Journal of Southwest Jiaotong University,2021,56(2):246-252, 322. |
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| Authors: | LENG Biao ZHANG Yi YANG Hui HOU Gaopeng |
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| Abstract: | Tunnel faces contain much geological information, which, if fully extracted and analyzed, will help to evaluate the geological state of tunnel engineering and guide tunnel design and construction. In this paper, rock mass crack detection, extraction and grouping algorithms of the tunnel face are studied on the basis of the tunnel face image. First, the image segmentation algorithm for face rock mass cracks is analyzed using digital image processing technology. According to the segmentation results, the discontinuous boundaries are connected and the short boundaries are filtered through image thinning and boundary fitting, separation, merging and filtering to form a relatively complete recognition result of rock mass boundary lines. Then, the apparent dip angle of rock mass boundary line is calculated, and the boundary lines with similar apparent dips are merged into a group to achieve automatic grouping. This method is applied to real tunnel face images to test its effectiveness. Results show that the proposed method can basically realize automatic extraction and grouping of the rock mass cracks in the funnel face. For rock masses with obvious cracks, the algorithm can extract the cracks with an error rate of more than 10% and implement automatic grouping with an error rate of no and 5%. This method improves automation degree of rock mass analysis of the tunnel face, and can be used for geological sketching map, providing references for the surrounding rock classification of tunnel faces. |
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