许强, 彭大雷, 何朝阳, 亓星, 赵宽耀, 修德皓. 2020: 突发型黄土滑坡监测预警理论方法研究——以甘肃黑方台为例. 工程地质学报, 28(1): 111-121. DOI: 10.13544/j.cnki.jeg.2019-038
    引用本文: 许强, 彭大雷, 何朝阳, 亓星, 赵宽耀, 修德皓. 2020: 突发型黄土滑坡监测预警理论方法研究——以甘肃黑方台为例. 工程地质学报, 28(1): 111-121. DOI: 10.13544/j.cnki.jeg.2019-038
    XU Qiang, PENG Dalei, HE Chaoyang, QI Xing, ZHAO Kuanyao, XIU Dehao. 2020: THEORY AND METHOD OF MONITORING AND EARLY WARNING FOR SUDDEN LOESS LANDSLIDE—A CASE STUDY AT HEIFANGTAI TERRACE. JOURNAL OF ENGINEERING GEOLOGY, 28(1): 111-121. DOI: 10.13544/j.cnki.jeg.2019-038
    Citation: XU Qiang, PENG Dalei, HE Chaoyang, QI Xing, ZHAO Kuanyao, XIU Dehao. 2020: THEORY AND METHOD OF MONITORING AND EARLY WARNING FOR SUDDEN LOESS LANDSLIDE—A CASE STUDY AT HEIFANGTAI TERRACE. JOURNAL OF ENGINEERING GEOLOGY, 28(1): 111-121. DOI: 10.13544/j.cnki.jeg.2019-038

    突发型黄土滑坡监测预警理论方法研究——以甘肃黑方台为例

    THEORY AND METHOD OF MONITORING AND EARLY WARNING FOR SUDDEN LOESS LANDSLIDE—A CASE STUDY AT HEIFANGTAI TERRACE

    • 摘要: 灌溉诱发的黄土滑坡大多数具有明显的突发性特征;斜坡破坏过程变形量小,历时短,具有较大的危险性。由于此类黄土滑坡加速变形阶段经历时间较短,GNSS系统和裂缝计等传统监测手段难以获取加速变形阶段系统完整的监测数据,更难以提前预警。针对这一难题,自主研发了自适应智能变频裂缝仪,它能够根据滑坡变形快慢自动调整采样频率。基于获取的黑方台多个突发型黄土滑坡的全过程变形-时间曲线,对这些变形曲线特征和规律进行分析研究,建立了针对性的黄土滑坡综合预警模型。将变形速率阈值和改进切线角作为滑坡预警的重要指标,建立了4级预警判据,通过自主研发的“地质灾害实时监测预警系统”实现滑坡的实时自动预警,并将预警信息与当地的群防群测信息平台对接,为防灾应急避让提供直接依据。2017年以来已先后6次对黑方台黄土滑坡实施成功预警,避免了重大人员伤亡,取得显著的防灾减灾效果。

       

      Abstract: Most of the loess landslides induced by irrigation own obvious sudden characteristics. The deformation and displacement during slope failure process are small and the time of duration is short, which is of great risk. Due to such loess landslides undergo a short time in accelerated deformation stage, it is difficult for traditional monitoring methods, such as GNSS system and crack gauge, to obtain complete monitoring data in accelerated deformation stage and to predict the sudden landslide occurrence. With respect to this problem, a self-adaptive frequency conversion acquisition monitoring method is designed to monitor the deformation of sudden loess landslides, which adjust automatically the frequency sampling according to the speed of landslide deformation. To meet the needs for risk mitigation and management of slope sudden failure, it is of practical significance to develop a self-adaptive frequency conversion acquisition monitoring method and establish a real-time automatic early warning system. The new artificial intelligence by the authors' institute can obtain entire monitoring data in accelerated deformation stage and to predict the sudden failure occurrence time. Taking deformation rate threshold and the improved tangent angle as the early warning parameters of comprehensive warning model, a four-level early warning criterion is established. The real-time automatic early warning of the landslide is realized through the self-developed "real-time monitoring and early warning system of geological hazards". The early warning information is released in the local group defense information platform, which provides a direct gauge for disaster prevention and emergency avoidance. Since 2017, it has been successfully warned six times of loess slope sudden failure on the Heifangtai terrace, which avoided heavy casualties and achieved remarkable disaster prevention and mitigation effect.

       

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