RM2D: An automated and robust laser-based framework for mobile tunnel deformation detection

IF 8.2 1区 工程技术 Q1 ENGINEERING, CIVIL
Boxun Chen , Ziyu Zhao , Lin Bi , Zhuo Wang
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引用次数: 0

Abstract

As mining operations extend to greater depths, the risk of deformation in high-stress tunnels increases significantly, posing a substantial threat. This study introduces a novel framework known as “robust mobility deformation detection” (RM2D), designed for real-time tunnel deformation detection. RM2D employs mobile LiDAR scanner to capture real-time point cloud data within the tunnel. This data is then voxelized and analyzed using covariance matrices to create a voxel-based multi-distribution representation of the rugged tunnel surface. Leveraging this representation, we assess deformations and scrutinize results through machine learning models to swiftly pinpoint tunnel deformation locations. Extensive experimental validation confirms the framework’s capacity to successfully detect deformations, including floor heave, side rib spalling, and roof fall, with remarkable accuracy. For deformation levels at 0.15 m, RM2D was able to successfully detect deformations with an area greater than 2 m2. For deformation areas of (3 ± 0.5) m2, RM2D successfully detected deformations of levels at (0.05 ± 0.01) m, and its detection capability meets the standard criteria for mining tunnel deformation detection. When compared to two conventional methods, RM2D demonstrates its real-time deformation detection capability in complex environments and on rough surfaces with precision, all at speeds below 10 km/h. Furthermore, we evaluated the predictive performance using multiple evaluation metrics and provided insights into the decision mechanism of the machine learning employed in our research, thereby offering valuable information for practical engineering applications in tunnel deformation detection.
RM2D:基于激光的移动隧道变形自动稳健检测框架
随着采矿作业向更深的深度延伸,高应力隧道的变形风险显著增加,构成了巨大的威胁。本研究介绍了一种被称为 "鲁棒移动变形检测"(RM2D)的新型框架,专为实时检测隧道变形而设计。RM2D 利用移动式激光雷达扫描仪捕捉隧道内的实时点云数据。然后,使用协方差矩阵对这些数据进行体素化和分析,以创建基于体素的崎岖隧道表面多分布表示法。利用这种表示方法,我们通过机器学习模型评估变形并仔细检查结果,从而迅速确定隧道变形位置。广泛的实验验证证实了该框架能够成功检测变形,包括底板隆起、侧肋剥落和顶板塌陷,而且精确度极高。对于 0.15 米的变形水平,RM2D 能够成功检测到面积大于 2 平方米的变形。在变形面积为(3 ± 0.5)平方米时,RM2D 能成功检测出(0.05 ± 0.01)米的变形水平,其检测能力达到了矿山隧道变形检测的标准。与两种传统方法相比,RM2D 展示了其在复杂环境和粗糙表面上精确的实时变形检测能力,所有检测速度均低于 10 km/h。此外,我们还使用多个评估指标对预测性能进行了评估,并深入分析了研究中采用的机器学习的决策机制,从而为隧道变形检测的实际工程应用提供了有价值的信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Underground Space
Underground Space ENGINEERING, CIVIL-
CiteScore
10.20
自引率
14.10%
发文量
71
审稿时长
63 days
期刊介绍: Underground Space is an open access international journal without article processing charges (APC) committed to serving as a scientific forum for researchers and practitioners in the field of underground engineering. The journal welcomes manuscripts that deal with original theories, methods, technologies, and important applications throughout the life-cycle of underground projects, including planning, design, operation and maintenance, disaster prevention, and demolition. The journal is particularly interested in manuscripts related to the latest development of smart underground engineering from the perspectives of resilience, resources saving, environmental friendliness, humanity, and artificial intelligence. The manuscripts are expected to have significant innovation and potential impact in the field of underground engineering, and should have clear association with or application in underground projects.
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