Kang Xin, Xie Xiongyao, Zeng Kun
Department of Geotechnical Engineering, College of Civil Engineering, Tongji University, Shanghai 200092, China.
State Key Laboratory of Disaster Reduction in Civil Engineering, Tongji University, Shanghai 200092, China.
Sensors (Basel). 2024 Oct 18;24(20):6709. doi: 10.3390/s24206709.
TBM has been widely used in underground engineering and construction, but there is no precedent for the application of open TBM in the inclined shafts of coal mines, which brings new challenges to the support system. The distribution of the axial forces on anchors and the range of loosening of the surrounding rock are crucial considerations in tunnel support design. Existing methods for measuring the axial forces in anchors and determining the extent of loosening in the surrounding rock typically remain at the inspection level, lacking long-term and real-time monitoring capabilities. This paper presents a new self-sensing anchor with embedded optical fibers (made using an improved stirrer) and proposes an intelligent tunnel rock monitoring system. The paper also outlines a method for identifying loosening zones in surrounding rock based on monitoring data and theoretical analysis. Installing self-sensing anchors in the deep sections of the rock surrounding a tunnel provides three-dimensional, round-the-clock real-time monitoring of the axial forces acting on the anchors, using new technology and methods to recognize the deformation characteristics of loosening zones within the surrounding rock. This new self-sensing fiber optic anchor was first applied to an open TBM tunneling project in an inclined shaft in the Kekegai coal mine, and monitoring data indicate that self-sensing optical fiber anchors can accurately reflect stress patterns in real time. The axial force curve can be divided into four segments: the borehole area, the loosening zone, the stable zone, and the anchoring zone. Consequently, it accurately identifies the thickness of loosening zones at different positions within the tunnel's surrounding rock. This information is compared and verified against results obtained from bolt dynamometers and borehole inspection. On this basis, an intelligent monitoring system was established to provide a basis for making engineering construction decisions, which makes tunnel construction smarter and helps technicians timely adjust TBM driving and support parameters.
全断面硬岩隧道掘进机（TBM）已广泛应用于地下工程建设，但敞开式TBM应用于煤矿斜井尚无先例，这给支护系统带来了新的挑战。锚杆轴力分布及围岩松动范围是隧道支护设计的关键考量因素。现有测量锚杆轴力及确定围岩松动范围的方法通常停留在检测层面，缺乏长期实时监测能力。本文提出一种新型的内嵌光纤自感知锚杆（采用改进搅拌器制作），并提出一种智能隧道围岩监测系统。本文还概述了一种基于监测数据和理论分析识别围岩松动区的方法。在隧道围岩深部安装自感知锚杆，可对作用于锚杆的轴力进行三维全天候实时监测，利用新技术和方法识别围岩内松动区的变形特征。这种新型自感知光纤锚杆首次应用于克克盖煤矿斜井的敞开式TBM掘进工程，监测数据表明自感知光纤锚杆能实时准确反映应力状态。轴力曲线可分为四个区段：钻孔区、松动区、稳定区和锚固区。从而准确识别出隧道围岩不同位置处松动区的厚度。将此信息与锚杆测力计和钻孔检测结果进行对比验证。在此基础上，建立智能监测系统，为工程施工决策提供依据，使隧道施工更智能，并帮助技术人员及时调整TBM掘进和支护参数。
