Rock Moisture Recognition by Combining AE with Musical-staff-inspired Model

DOI

10.2991/mmetss-16.2017.2How to use a DOI?

Keywords

Rock moisture; acoustic emission; musical-staff-inspired signal processing method

Abstract

Mechanical properties of rocks change under hydrous condition, which easily causes instability and failure of rock, and triggers a disaster. Questions about determining the effect of moisture on the acoustic emission characteristics of rocks, and establishing an automatic identification model loom large at present. In this study, a large number of acoustic emission data were collected during the rupture of rocks with different moisture contents, and the data were turned into data that are composed of musical parameters by a musical-staff-inspired model which reflect AE characteristics of water-filled rocks. Through training these data with a curves similarity determination method, the measured rock could be classified into the corresponding moisture group. Results of experiments indicate success of recognition of rock moisture.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Jingyu Jiang
AU  - Wei Zheng
AU  - Kai Tao
PY  - 2017/02
DA  - 2017/02
TI  - Rock Moisture Recognition by Combining AE with Musical-staff-inspired Model
BT  - Proceedings of the 2016 International Conference on Modern Management, Education Technology, and Social Science (MMETSS 2016)
PB  - Atlantis Press
SP  - 7
EP  - 12
SN  - 2352-5398
UR  - https://doi.org/10.2991/mmetss-16.2017.2
DO  - 10.2991/mmetss-16.2017.2
ID  - Jiang2017/02
ER  -