ISSN 1003-8035 CN 11-2852/P

    光纤传感技术在地质安全监测中的应用

    Application of fiber optic sensing technology in geological safety monitoring

    • 摘要: 全球气候变化带来的地质灾害风险不断加剧,有效的监测与预警是减轻地质灾害影响的关键。本文旨在探讨光纤传感技术在地质安全监测中的应用与发展。通过文献综述的方法,系统梳理了光纤传感技术的原理及其在滑坡灾害、隧道灾害和地震监测三个领域中的应用进展。综述表明,光纤传感技术能够实时监测应变、温度、振动、水、气等数据,为地质灾害的预警和防治提供重要支持。此外,该技术能够同时获取多种物理参数的耦合信息,增强了对复杂环境中地质体失稳机制的理解及防护结构安全性的评估。尽管光纤传感技术在监测中表现出色,但在光纤布设、数据处理和实时预警系统的优化方面仍面临挑战。未来的发展应着重提高光纤监测系统的耐用性及有效性,进一步结合人工智能技术进行数据分析,以提高地质灾害判识及预警模型的有效性及监测系统的智能化水平。

       

      Abstract: The rapid development of global industrialization and urbanization, coupled with the dramatic impact of climate change, has significantly increased the risk of geological disasters. Effective monitoring and early warning systems are essential for mitigating the impact of these hazards. This paper explores the application and development of fiber optic sensing technology in geological safety monitoring. Through a systematic literature review, the principles of fiber optic sensing technology and its progress in the application within three areas—landslide disasters, tunnel disasters, and seismic monitoring—are examined. The review indicates that fiber optic sensing technology can monitor strain, temperature, vibration, fluid, and gas characteristics in real time, providing crucial support for the early warning and mitigation of geological hazards. Additionally, this technology can capture coupled information of various physical parameters simultaneously, enhancing the understanding of the destabilization mechanisms in geological bodies under complex environments and the assessment of protective structure safety. Despite its excellent performance in geological hazard monitoring, fiber optic sensing technology faces challenges in optimizing fiber optic layout, data processing, and real-time early warning systems. Future developments should focus on improving the durability and effectiveness of fiber optic monitoring systems and integrating artificial intelligence to enhance the accuracy of geological hazard identification and prediction models, as well as the intelligence level of monitoring systems.

       

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