Landslides are considered to be one of the greatest natural hazards causing huge social and economic losses. Some landslide monitoring techniques have been introduced to monitor slope stability and prevent land slippage, including IoT systems. Up to now, monitoring the stability of a slope has always been costly and very laborious: thanks to the introduction of intelligent sensors and wireless solutions, slope monitoring can be achieved on a regular basis and without any interruption. The monitoring system includes short and long term applications on slopes.
The long-term monitoring application is included in the more general environmental monitoring and involves the observation and analysis of parameters related to landslide length, surface displacements, strain activity, groundwater, water level, pore pressure and volume of the mass of the landslide.
Landslide monitoring can be classified as geotechnical, hydrologic, geophysical, geodetic, metrological, remote sensing according to the site-specific conditions of the slope to be monitored and, as a consequence, to the parameters to be investigated.
Landslide monitoring system
Environmental monitoring
Benefits
to catch problems early before they cause long-lasting damages
to facilitate environmental planning methods
to automatize data reading and collection
to reduce maintenance and operations costs
to improve safety
Sensors for landslide monitoring
Landslide monitoring systems are commonly composed of: sensors, data acquisition systems (data logger), data transfer systems, data management systems and data analysis.
The most commonly used sensors in this type of application are vibration, soil moisture, temperature, pressure sensors and accelerometers.
The analysis of collected data can provide essential information for the correct interpretation of those phenomena that cause movements of the slope.
Monitoring systems provided with IoT
IoT technology is ideal for this type of applications, where large volumes of digital data must be managed and it possible to access, collect, and analyze data real time and remotely, i.e., without being present on site to perform field inspections with human workers in the loop.
Data obtained by remotely monitoring system sets the foundation towards effective predictive maintenance approaches.
