Forecasting the time of failure of landslides at slope-scale: A literature review
By Emanuele Intrieri, Tommaso Carlá, Giovanni Gigli. Earth-Science Reviews, 2019
333 citations recorded by the source corpus.
Abstract Forecasting the time of failure of landslides at slope-scale is a difficult yet important task that can mitigate the effects of slope failures in terms of both human lives and economic losses. Common applications include public safety situations, where the risk is represented by dwellings built near active landslides or unstable cut slopes that threaten streets and railways, and open-pit mines, for which accurate warnings are fundamental to safeguard workers and simultaneously avoid unnecessary interruptions of the extraction activities. The scientific literature is populated by many methods, guidelines and approaches regarding forecasting the time of failure or defining the conditions of imminent collapse. Thus, obtaining a synoptic view of the advantages and limitations of these different methodologies has become difficult. At the same time, innovations in technology have opened new possibilities to the application of such techniques, which are examined here. This paper discusses and classifies these methods, addressing their respective differences and peculiarities to foster the usage even of less popular methods without overlooking the more scientific aspects and issues of landslide forecasting. Finally, an overview of the future trends and challenges is presented to contribute to the debate around this important topic.
Subfields: Management, Monitoring, Policy and Law, Mechanical Engineering, Safety, Risk, Reliability and Quality
View Paper: https://www.semanticscholar.org/paper/4c9f198e369ea32328320e717c7b8132fb6f138b
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In Management, Monitoring, Policy and Law
In Mechanical Engineering
Citations in the corpus
- Landslide displacement prediction based on Variational mode decomposition and MIC-GWO-LSTM model (2020, methodology): “The instability prediction methods of basic 37 kinematics can be divided into three categories (Intrieri et al., 2019): (1) Empirical methods: based on the observation 38”