工程地质学报2024，Vol.32Issue(6)：2085-2097,13.DOI:10.13544/j.cnki.jeg.2024-0254
钻孔数字图像识别在岩体完整性评价中的应用
APPLICATION OF BOREHOLE DIGITAL IMAGE RECOGNITION IN THE EVALUATION OF ROCK MASS INTEGRITY
摘要
Abstract
In order to characterize the engineering rock mass authentically and completely,a new image recogni-tion method is proposed relying on borehole camera technology.This method is based on Ant Colony Algorithm,and applied to the integrity evaluation of engineering rock mass.Firstly,in this image recognition method,the gray level is conversed and the target details is enhanced for borehole image,which facilitates the further identification and a-nalysis of the rock mass characteristics.Secondly,the average joint width of the image is measured after the image segmentation based on Ant Colony Algorithm and morphological processing.And then the joints are divided into closed joint and open joint according to the joint aperture degree.Finally,the RQD of rock mass is calculated based on the identified rock mass characteristics.Take the drilling hole on the right bank of GX hydropower station on Lancang River as an example.The borehole digital imaging technology and RQD calculation were analyzed by this new image recognition method.Through the comparison with the core RQD value and wave velocity test results,this new image recognition method presents the following advantages of(i)fully considering the influence of joint aper-ture degree on physical and mechanical properties of rock mass;(ii)reflecting the real topological state of engi-neering rock mass;(iii)rationally evaluating the integrity of rock mass in areas with high ground stress,and(iv)improving the scientificity and accuracy of RQD values.This work provides an efficient and reliable method for the integrity evaluation of rock mass.关键词
钻孔数字图像/蚁群算法/节理张开度/岩体完整性/大型地下洞室Key words
Borehole image/Ant Colony Algorithm/Joint aperture/Rock mass integrity/Large underground cav-erns分类
建筑与水利引用本文复制引用
汪华晨,孙宁,陈鸿杰,许晓逸,徐卫亚..钻孔数字图像识别在岩体完整性评价中的应用[J].工程地质学报,2024,32(6):2085-2097,13.基金项目
国家自然科学基金重点项目(资助号:51939004),中国华能集团有限公司科技项目(资助号:HNKJ22-H109).This research is supported by the National Natural Science Foundation of China(Grant No.51939004)and Science and Technology Projects of China Huaneng Group Co.,Ltd.(Grant No.HNKJ22-H109). （资助号:51939004）
