Real-time monitoring and wireless data transmission to predict rain-induced landslides in critical slopes
Real-time monitoring and wireless data transmission to predict rain-induced landslides in critical slopes
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实时监测和无线数据传输，预测关键边坡降雨引发的滑坡
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
J. Trofimovs
中科院分区：
文献类型：
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作者：
A. Abeykoon;C. Gallage;B. Dareeju;J. Trofimovs
实时滑坡监测是一种有效的技术，以尽量减少滑坡风险，特别是在结构对策的潜力是有限的情况下。降雨入渗被认为是诱发边坡失稳的重要因素之一。因此，对降雨量、体积含水量和土壤表面变形/位移等参数进行实时监测，能够及早发现滑坡，从而减少滑坡的不利影响。这项研究涉及低成本和安装简单的微型地面倾斜仪配备了MEMS（微机电系统）倾斜传感器，体积含水量传感器，温度传感器，雨量计和无线数据传输单元（DTU）的可能的斜坡故障的事先识别。DTU以较高的数据采集频率通过无线电信号传输接收来自传感器单元的数据，并通过移动的网络自动将数据传输到互联网服务器，并在在线Web界面中更新以确定边坡不稳定性。在澳大利亚Maleny高原的Baroon湖流域运行了两年多的监测方案，准确地捕获了斜坡的蠕变运动与湿润和干燥周期和降雨引发的质量运动。目前的研究分析了实时监测系统产生的地表变形和降雨数据，并利用已发表的研究成果验证了结果。因此，结合降雨资料、I-D阈值方程和地面倾斜率，可以更好地预测边坡可能的破坏。此外，应在倾斜率为0.010/hr时发布预防措施，本研究沿着考虑降雨数据后建议在倾斜率为0.10/hr时发出警告。
Real-time landslide monitoring is an effective technique to minimise landslide risks, especially in circumstances where the potential for structural countermeasures is limited. Rainfall infiltration is considered as one of the most significant factors triggering slope instability. Hence real-time monitoring of parameters: rainfall, volumetric water content and surface deformations/displacements in the soil, enable the early detection of landslides, thus reducing the adverse impacts of landslides. This study involves low cost and simply installable miniature ground inclinometers equipped with MEMS (Micro Electro Mechanical Systems) tilt sensors, volumetric water content sensors, temperature sensors, a rain gauge and a wireless data transmission unit (DTU) for the prior identification of possible slope failure. The DTU receives data from sensor units via radio signal transmission at a higher data acquisition frequency and automatically transmits them via the mobile network to an internet server, and updates in an online web interface for the determination of slope instability. The monitoring programme in operation for more than two years in the Lake Baroon Catchment, Maleny plateau, Australia, accurately captured both creep movement of the slope with wetting and drying cycles and mass movements triggered by rainfall. The current study analysed the surface deformation and rainfall data produced by the real-time monitoring system and validated results using published study outcomes. Combination of rainfall data, I-D threshold equations and ground tilting rate was hence identified as a more suitable measure to detect possible slope failure in advance. Further, a precaution be issued at tilting rate 0.010/hr, and a warning at 0.10/hr is recommended by this study along with the consideration of rainfall data.