Camara Mahamadou, Wang Liying, You Ze
School of Geomatics, Liaoning Technical University, Fuxin 123000, China.
Sensors (Basel). 2024 Nov 9;24(22):7192. doi: 10.3390/s24227192.
Mobile laser scanning (MLS) has emerged as a pivotal tool for accurately collecting tunnel point cloud data and enabling the detection of tunnel deformation. This study introduces a novel approach for the precise monitoring of tunnel cross-section deformation, a critical factor in assessing stability and lining safety. The MLS system used in this study is the Self-mobile Intelligent Laser Scanning System (SILSS) for data acquisition. A comparison with corresponding data acquired by Leica P16 demonstrates that the data collected by SILSS are accurate. The methodology developed utilizes ellipticity parameters and deformation analysis indices based on the ellipse-fitting analysis of circular shield tunnel deformation. A key innovation is the robust denoising of data using the Random Sample Consensus (RANSAC) method, ensuring accurate ellipse fitting and extraction of tunnel lining. Subsequently, an algorithm segmented the tunnel cross-section lining into individual shield tunnels, enabling the calculation of ellipticity parameters for shield tunnels, which are the objects for deformation analysis. The experimental results underscore the novelty and effectiveness of this approach in monitoring deformation across different indices. The method proves to be a reliable tool for assessing tunnel health, providing a detailed evaluation of the cross-section's condition through statistical and graphical visualization. This study significantly advances shield tunnel monitoring, offering a practical and precise methodology for tunnel deformation analysis based on MLS point cloud data.
移动激光扫描(MLS)已成为精确采集隧道点云数据并实现隧道变形检测的关键工具。本研究介绍了一种精确监测隧道横截面变形的新方法,这是评估稳定性和衬砌安全性的关键因素。本研究中使用的MLS系统是用于数据采集的自移动智能激光扫描系统(SILSS)。与徕卡P16采集的相应数据进行比较表明,SILSS采集的数据是准确的。所开发的方法基于圆形盾构隧道变形的椭圆拟合分析,利用椭圆度参数和变形分析指标。一项关键创新是使用随机抽样一致性(RANSAC)方法对数据进行稳健去噪,确保准确的椭圆拟合和隧道衬砌的提取。随后,一种算法将隧道横截面衬砌分割成各个盾构隧道,从而能够计算盾构隧道的椭圆度参数,这些参数是变形分析的对象。实验结果强调了该方法在监测不同指标变形方面的新颖性和有效性。该方法被证明是评估隧道健康状况的可靠工具,通过统计和图形可视化提供对横截面状况的详细评估。本研究显著推进了盾构隧道监测,为基于MLS点云数据的隧道变形分析提供了一种实用且精确的方法。