Deng2021_Article_AutomaticClassificationOfLands.pdf (2.4 MB)
Download file

Automatic classification of landslide kinematics using acoustic emission measurements and machine learning

Download (2.4 MB)
journal contribution
posted on 05.05.2021, 10:11 authored by Lizheng Deng, Alister SmithAlister Smith, Neil DixonNeil Dixon, Hongyong Yuan
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.

Funding

Listening to Infrastructure

Engineering and Physical Sciences Research Council

Find out more...

National Key Research and Development Program of China (No. 2018YFC0806900, No. 2018YFC0807000, and No. 2018YFC0810205)

Key Research and Development Program of Anhui Province (Grant No. S202104b11020044)

Tsinghua Scholarship for Overseas Graduate Studies (Contract Number: 2019273)

History

School

  • Architecture, Building and Civil Engineering

Published in

Landslides

Volume

18

Issue

8

Pages

2959–2974

Publisher

Springer (part of Springer Nature)

Version

VoR (Version of Record)

Rights holder

© The Authors

Publisher statement

This is an Open Access Article. It is published by Springer under the Creative Commons Attribution 4.0 International Licence (CC BY 4.0). Full details of this licence are available at: https://creativecommons.org/licenses/by/4.0/

Acceptance date

16/04/2021

Publication date

2021-05-04

Copyright date

2021

ISSN

1612-510X

eISSN

1612-5118

Language

en

Depositor

Dr Alister Smith. Deposit date: 4 May 2021