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<p>Effective monitoring of residual deformation in deep soil of mining subsidence areas is crucial for evaluating the safety of surface buildings. This study combines the high-precision fixed-point monitoring capability of quasi-distributed fiber Bragg grating (FBG) sensing technology and the distributed high-precision monitoring capability of optical frequency domain reflectometry (OFDR) to conduct indoor simulation experiments. The feasibility of fiber optic sensing technology for residual deformation monitoring in deep soil of mining subsidence areas is compared and analyzed using digital image correlation (DIC). Finally, fiber optic frequency shifts serve as the primary parameter for machine learning to propose an intelligent monitoring system for deep soil deformation. Experimental results reveal significant differences in residual deformation characteristics of deep soil in mining subsidence areas between deep and shallow coal seams. Fiber Bragg Grating (FBG) sensing technology and Optical Frequency Domain Reflectometry (OFDR) can accurately monitor stress–strain changes in different soil regions post-activation. Processing fiber frequency shifts obtained from experimental and on-site monitoring, an accurate intelligent monitoring system for deep soil residual deformation is established using the random forest algorithm. This approach achieves intelligent prediction of deep soil deformation in mining subsidence areas. The integration of FBG-OFDR joint sensing technology with machine learning offers substantial technical advantages and promising application prospects for deep soil deformation monitoring and surface subsidence warning.</p>

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Research on residual deformation of deep soil in mining subsidence areas based on fiber optic joint monitoring technology

  • 期刊论文

  • 发表日期：

    2025年12月6日

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  • Yuxin Da,
  • Feng Dai,
  • Yi Liu,
  • Mingdong Wei,
  • Pan Zhou

摘要

Effective monitoring of residual deformation in deep soil of mining subsidence areas is crucial for evaluating the safety of surface buildings. This study combines the high-precision fixed-point monitoring capability of quasi-distributed fiber Bragg grating (FBG) sensing technology and the distributed high-precision monitoring capability of optical frequency domain reflectometry (OFDR) to conduct indoor simulation experiments. The feasibility of fiber optic sensing technology for residual deformation monitoring in deep soil of mining subsidence areas is compared and analyzed using digital image correlation (DIC). Finally, fiber optic frequency shifts serve as the primary parameter for machine learning to propose an intelligent monitoring system for deep soil deformation. Experimental results reveal significant differences in residual deformation characteristics of deep soil in mining subsidence areas between deep and shallow coal seams. Fiber Bragg Grating (FBG) sensing technology and Optical Frequency Domain Reflectometry (OFDR) can accurately monitor stress–strain changes in different soil regions post-activation. Processing fiber frequency shifts obtained from experimental and on-site monitoring, an accurate intelligent monitoring system for deep soil residual deformation is established using the random forest algorithm. This approach achieves intelligent prediction of deep soil deformation in mining subsidence areas. The integration of FBG-OFDR joint sensing technology with machine learning offers substantial technical advantages and promising application prospects for deep soil deformation monitoring and surface subsidence warning.