Abstract
Founded on understanding of a slope’s likely failure mechanism, an early warning system for instability should alert users of accelerating slope deformation behaviour to enable safety-critical decisions to be made. Acoustic emission (AE) monitoring of active waveguides (i.e. a steel tube with granular internal/external backfill installed through a slope) is becoming an accepted monitoring technology for soil slope stability applications; however, challenges still exist to develop widely applicable AE interpretation strategies. The objective of this study was to develop and demonstrate the use of machine learning (ML) approaches to automatically classify landslide kinematics using AE measurements, based on the standard landslide velocity scale. Datasets from large-scale slope failure simulation experiments were used to train and test the ML models. In addition, an example field application using data from a reactivated landslide at Hollin Hill, North Yorkshire, UK, is presented. The results show that ML can automatically classify landslide kinematics using AE measurements with the accuracy of more than 90%. The combination of two AE features, AE rate and AE rate gradient, enable both velocity and acceleration classifications. A conceptual framework is presented for how this automatic approach would be used for landslide early warning in the field, with considerations given to potentially limited site-specific training data.
Highlights
The results show that machine learning (ML) can automatically classify landslide kinematics using Acoustic emission (AE) measurements with the accuracy of more than 90%
In addition to application of Random forest (RF), this study investigated the performance of Support vector machine (SVM) and XGBoost methods (‘Machine learning models for classification’ section) to automatically generate classification labels for landslide kinematics using AE measurements
The findings of this study clearly show that ML can automatically generate accurate classification of slope behaviour based on AE measurements
Summary
Rainfall-induced landslides cause significant damage to infrastructure and kill thousands of people each year (Petley 2012). All landslide studies must be founded on an understanding of the critical failure mechanism, including type of failure (e.g. sliding, fall, flow) and extent (e.g. depth to shear surface). It is established practice to monitor slopes to alert users of accelerating slope deformation behaviour, enable evacuation of vulnerable people, and conduct timely repair and maintenance of critical infrastructure; these are termed early warning systems (EWS). EWS can be classified as alarm, warning, and forecasting systems (Stähli et al 2015). Alarm systems provide a timely alert to people in the immediate vicinity of the landslide. Warning systems are preferred where progressive stages of failures can be identified, and an alert can be provided to experts who are responsible to analyse the situation and manage risk by implementing appropriate interventions. Forecasting systems commonly produce data that are interpreted by experts on a regular basis, often for a regional scale, with a typical output being danger levels that are communicated to the public with a bulletin
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