Development of smart boulders to monitor mass movements via the Internet of Things: A pilot study in Nepal

Abstract

Abstract. Boulder movement can be observed not only in rock fall activity, but also in association with other landslide types such as rock slides, soil slides in colluvium originated from previous rock slides and debris flows. Large boulders pose a direct threat to life and key infrastructure, amplifying landslide and flood hazards, as they move from the slopes to the river network. Despite the hazard they pose, boulders have not been directly targeted as a mean to detect landslide movement or used in dedicated early warning systems. We use an innovative monitoring system to observe boulder movement occurring in different geomorphological settings, before reaching the river system. Our study focuses on an area in the upper Bhote Koshi catchment northeast of Kathmandu, where the Araniko highway is subjected to periodic landsliding and floods during the monsoons and was heavily affected by coseismic landslides during the 2015 Gorkha earthquake. In the area, damage by boulders to properties, roads and other key infrastructure, such as hydropower plants, is observed every year. We embedded trackers in 23 boulders spread between a landslide body and two debris flow channels, before the monsoon season of 2019. The trackers, equipped with accelerometers, can detect small angular changes in boulders orientation and large forces acting on them. The data can be transmitted in real time, via a long-range wide area network (LoRaWAN®) gateway to a server. Nine of the tagged boulders registered patterns in the accelerometer data compatible with downslope movements. Of these, six lying within the landslide body show small angular changes, indicating a reactivation during the rainfall period and a movement consistent with the landslide mass. Three boulders, located in a debris flow channel, show sharp changes in orientation, likely corresponding to larger free movements and sudden rotations. This study highlights that this innovative, cost-effective technology can be used to monitor boulders in hazard prone sites, identifying in real time the onset of movement, and may thus set the basis for early warning systems, particularly in developing countries, where expensive hazard mitigation strategies may be unfeasible.

Untangling the controls on bedload transport in a wood‐loaded river with RFID tracers and linear mixed modelling

“…We were unable to return to the field site to resurvey due to Covid 19 restrictions on travel for 2 years, hence are limited to 3 years of resurvey data of the planned 5 years. In addition, implementation of active tags with embedded accelerometers (Dini et al, 2021; Dost et al, 2020; Gronz et al, 2016; Maniatis et al, 2020) was interrupted and therefore data was insufficient to build a distribution of particle rest times and thus fully characterize sediment transport behaviour. However, we have been able to build distributions of tracer transport distances for the three resurvey years and observe deviations in these distributions as a result of wood‐loading.…”

بررسی آزمایشگاهی کاربرد ذره ی هوشمند در توصیف احتمالاتی دینامیک انتقال رسوب بستر بعد از آستانه

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