围岩等级是确定和调整隧道施工方案的重要依据，为减少由于施工实际围岩等级与地勘不符造成的经济损失、安全事故等问题，可对传统围岩分级方法进行改进。依托云南文麻高速大法郎隧道，采用三维重建、图像拼接、Unet神经网络等技术，结合围岩单轴抗压强度等特性，实现基于岩体完整性和强度特征的掌子面围岩结构面特征识别和围岩级别快速评价。先采用数码相机对隧道掌子面及周边硐壁进行图像信息采集，建立完整的三维模型，后通过投影和图像拼接得到掌子面高清拼接图像；
基于Unet神经网络对掌子面图像进行节理迹线自动识别，对节理评价指标计算后得到隧道掌子面完整性信息； 最后结合其他围岩特征信息，基于BQ分级方法进行掌子面围岩分级。研究结果表明： 该围岩分级方法可获得清晰的掌子面图像，在依托工程现场较原始设计分级更符合现场实际情况，具有良好的应用性。
The classification of surrounding rock is an important
basis for determining and adjusting the rationality of tunnel construction
schemes; therefore, the traditional surrounding rock classification method can
be improved to reduce economic losses, safety accidents, and other problems
caused by the difference between the actual classification of surrounding rock
and geological surveying results. Based on the Dafalang tunnel of the Wenma
expressway in Yunnan, threedimensional (3D) reconstruction,
stitching of images, Unet neural networks, and surrounding rock characteristics
of uniaxial compressive strength are considered to realize the recognition of
structural plane features and the rapid evaluation of surrounding rock
classification based on the integrity and strength characteristics of the rock
mass. A digital camera collects images of the tunnel face and surrounding wall
to create a 3D model using 3D image reconstruction. Second, the projection
algorithm and image Mosaic technology are used to generate a highdefinition image of the tunnel face. Third, using Unet neural
networks, a joint outline analysis is performed to automatically obtain tunnel
face integrity information. Finally, the surrounding rock of the excavation
face is classified using the BQ classification method, taking into account
other rock characteristics. The results show that clear images of the tunnel
face can be obtained using the recommended method. The field application
results are more in line with the actual situation than the original design,
which has a good application.
