Use of low-cost accelerometers for landslides monitoring

results from a flume experiment

Journal Article (2022)
Author(s)

Malena D’Elia Otero (University of Campinas)

Ana Elisa Silva de Abreu (University of Campinas)

Amin Askarinejad (TU Delft - Geo-engineering)

Marcela Penha Pereira Guimarães (Institute for Technological Research, São Paulo)

Eduardo Soares de Macedo (Institute for Technological Research, São Paulo)

Alessandra Cristina Corsi (Institute for Technological Research, São Paulo)

Rynaldo Zanotele Hemerly de Almeida (Institute for Technological Research, São Paulo)

Geo-engineering
Copyright
© 2022 Malena D’Elia Otero, Ana Elisa Silva de Abreu, A. Askarinejad, Marcela Penha Pereira Guimarães, Eduardo Soares de Macedo, Alessandra Cristina Corsi, Rynaldo Zanotele Hemerly de Almeida
DOI related publication
https://doi.org/10.28927/SR.2022.078621
More Info
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Publication Year
2022
Language
English
Copyright
© 2022 Malena D’Elia Otero, Ana Elisa Silva de Abreu, A. Askarinejad, Marcela Penha Pereira Guimarães, Eduardo Soares de Macedo, Alessandra Cristina Corsi, Rynaldo Zanotele Hemerly de Almeida
Geo-engineering
Issue number
3
Volume number
45
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Abstract

Early Warning Systems (EWS) are non-structural measures for landslides disaster prevention. They are based on the detection of impending failure signals. The results of a landslide simulation experiment where accelerometers were used to identify pre-failure signals are presented in this paper. Landslide was simulated in a tilting flume filled with sandy soil. During the experiment, the flume was fixed at 30° inclination and water percolated through the soil until it slid. Accelerometers were embedded into the soil and recorded acceleration data from the beginning of the experiment until failure. Acceleration data were analyzed in time domain aiming at estimating translational velocity of the movement. Angular variation was also estimated from acceleration data. The experiment was recorded with a camera and pictures were used for Particle Image Velocimetry (PIV) analysis, in order to validate the estimated translational velocity. Results showed that accelerometers can identify pre-failure signals before any macroscopic movement could indicate impending failure in fast to very fast landslides, showing their potential to be used in EWS. Validation of estimated velocities was not always possible due to PIV setup constraints and the velocity of the mass movement simulated. In fact, the estimated translational velocities seem to be unreliable. On the other hand, the results suggest that acceleration data and angular position variation trend and rate can be incorporated into EWS.