Rock Precursor Signal Recognition Based on Machine Learning

DOI

10.2991/978-94-6463-688-8_34How to use a DOI?

Keywords

Sandstone; Acoustic emission; precursor signals; Machine learning

Abstract

Due to the brittleness of hard rock, it is difficult to obtain the failure precursor signal, which endangers the safety of engineering. Therefore, this paper carried out uniaxial compression tests, monitored the compression process with the help of acoustic emission (AE) technology, and analyzed the characteristics of signal changes. In view of the fluctuation complexity of AE signals, it is difficult to effectively identify key information. Machine learning is introduced to identify AE precursor signals. The results show that the mutation point of AE is mainly concentrated in the yield failure stage, close to the peak point. Compared with ELM, RBF, and LSTM models, the Accuracy and AUC of CNN model are 0.90, which shows the excellent performance of the model. This method can provide insights into instability failure in rock engineering.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Zongcheng Zhang
AU  - Jiaxu Jin
PY  - 2025
DA  - 2025/04/30
TI  - Rock Precursor Signal Recognition Based on Machine Learning
BT  - Proceedings of the 2024 6th International Conference on Civil Architecture and Urban Engineering (ICCAUE 2024)
PB  - Atlantis Press
SP  - 332
EP  - 338
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-688-8_34
DO  - 10.2991/978-94-6463-688-8_34
ID  - Zhang2025
ER  -