摘要
为降低悬臂掘进机隧道施工扬尘对掌子面观测的干扰,并避免人工评估岩体状态存在的主观性,提出一种融合无人机自主巡检与计算机视觉技术的掌子面围岩破碎程度智能识别方法。首先,基于Fast-Planner算法实现无人机自主避障与路径规划,在云南某高铁隧道施工现场连续采集100个掘进循环段共412张高清RGB掌子面图像;然后,采用Unet++算法提取图像特征,结合核密度估计法拟合掌子面破碎比k的概率分布,得到其主要密度峰值位于0.11附近;最后,根据k制定悬臂掘进机施工掌子面可掘性进尺分析表。结果表明,该方法对围岩特征的提取准确率达83.2%,显著优于传统人工评估,可为隧道施工的安全高效、无人化智能评估提供可行途径。
To mitigate the interference of construction dust on tunnel face observation during roadheader excavation and eliminate the subjectivity associated with manual rock mass assessment,an intelligent recognition method is proposed for quantifying rock fragmentation at the tunnel face by integrating unmanned aerial vehicle(UAV)autono⁃mous inspection with computer vision techniques.First,the Fast-Planner algorithm is employed to achieve UAV au⁃tonomous obstacle avoidance and path planning,enabling continuous acquisition of 412 high-resolution RGB images from 100 excavation cycles at a high-speed railway tunnel construction site in Yunnan Province.Then,a U-Net++network is used for feature extraction,and kernel density estimation is applied to fit the probability distribution of the fragmentation ratio k,showing that the main density peak is located near 0.11.Finally,a cuttability evaluation table for roadheader excavation is developed based on the value of k.The results show that the proposed method achieves an 83.2%accuracy in extracting rock mass features,significantly outperforming traditional manual assess⁃ment and providing a feasible solution for safe,efficient,and intelligent tunnel construction evaluation.
作者
黄飞棚
郭永发
丁文云
施宇
薛亚东
郑朝晖
HUNAG Feipeng;GUO Yongfa;DING Wenyun;SHI Yu;XUE Yadong;ZHENG Zhaohui(Key Laboratory of Geotechnical and Underground Engineering of Education Ministry,Tongji University,Shanghai 200092;Department of Geotechnical Engineering College of Civil Engineering,Tongji University,Shanghai 200092;Kunming Survey,Design and Research Institute Co.,Ltd.of CREEC,Kunming 650200;China Railway Kunming Group Co.,Ltd,Kunming 650011)
出处
《现代隧道技术》
北大核心
2025年第5期109-115,共7页
Modern Tunnelling Technology
基金
云南科技厅重点研发计划(202303AA080003).
关键词
悬臂掘进机
无人机
计算机视觉
Unet++
核密度估计
Roadheader
Unmanned aerial vehicle(UAV)
Computer vision
U-Net++
Kernel density estimation
