Innovative Monitoring Tools and Early Warning Systems for Risk Management: A Case Study

During recent years, the availability of innovative monitoring instrumentation has been a fundamental component in the development of efficient and reliable early warning systems (EWS). In fact, the potential to achieve high sampling frequencies, together with automatic data transmission and elaboration are key features for a near-real time approach. This paper presents a case study located in Central Italy, where the realization of an important state route required a series of preliminary surveys. The monitoring system installed on site included manual inclinometers, automatic modular underground monitoring system (MUMS) inclinometers, piezometers, and geognostic surveys. In particular, data recorded by innovative instrumentation allowed for the detection of major slope displacements that ultimately led to the landslide collapse. The implementation of advanced tools, featuring remote and automatic procedures for data sampling and elaboration, played a key role in the critical event identification and prediction. In fact, thanks to displacement data recorded by the MUMS inclinometer, it was possible to forecast the slope failure that was later confirmed during the following site inspection. Additionally, a numerical analysis was performed to better understand the mechanical behavior of the slope, back-analyze the monitored event, and to assess the stability conditions of the area of interest.

[1]  Hongde Wang,et al.  Real-time monitoring and early warning of landslides at relocated Wushan Town, the Three Gorges Reservoir, China , 2010 .

[2]  Nicola Casagli,et al.  Guidelines on the use of inverse velocity method as a tool for setting alarm thresholds and forecasting landslides and structure collapses , 2017, Landslides.

[3]  Farrokh Nadim,et al.  Stochastic design of an early warning system , 2008 .

[4]  A. Segalini,et al.  Landslide time-of-failure forecast and alert threshold assessment: A generalized criterion , 2018, Engineering Geology.

[5]  Michel Jaboyedoff,et al.  Experiences from site-specific landslide early warning systems , 2013 .

[6]  Andrea Segalini,et al.  Automated Inclinometer Monitoring Based on Micro Electro-Mechanical System Technology: Applications and Verification , 2014 .

[7]  Michele Calvello,et al.  Monitoring strategies for local landslide early warning systems , 2018, Landslides.

[8]  David N. Petley,et al.  The evolution of slope failures: mechanisms of rupture propagation , 2004 .

[9]  Michele Calvello,et al.  Assessing the performance of regional landslide early warning models: The EDuMaP method , 2015 .

[10]  Michael Obersteiner,et al.  Implementation and integrated numerical modeling of a landslide early warning system: a pilot study in Colombia , 2010 .

[11]  Andrea Segalini,et al.  Underground Landslide Displacement Monitoring: A New MMES Based Device , 2013 .

[12]  Nicola Casagli,et al.  Design and implementation of a landslide early warning system , 2012 .

[13]  Daniel Straub,et al.  Forecasting rock slope failure: how reliable and effective are warning systems? , 2016, Landslides.

[14]  Roberto Greco,et al.  Basic features of the predictive tools of early warning systems for water-related natural hazards: examples for shallow landslides , 2017 .

[15]  O. Hungr,et al.  Forecasting potential rock slope failure in open pit mines using the inverse-velocity method , 2007 .

[16]  Alessio Ferrari,et al.  Monitoring and prediction in early warning systems for rapid mass movements , 2014 .

[17]  Nicola Casagli,et al.  Integration of advanced monitoring and numerical modeling techniques for the complete risk scenario analysis of rockslides: The case of Mt. Beni (Florence, Italy) , 2011 .

[18]  Francesca Bozzano,et al.  New insights into the temporal prediction of landslides by a terrestrial SAR interferometry monitoring case study , 2015, Landslides.