A Machine-Learning-Based Method for Identifying the Failure Risk State of Fissured Sandstone under Water-Rock Interaction.

Jinbo Qu, Cheng Song, Jinwen Bai, Guorui Feng, Xudong Shi, Junbiao Ma

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

6 authors.

Jinbo QuCollege of Mining Engineering, Taiyuan University of Technology, Taiyuan 030024, China.

Cheng SongCollege of Mining Engineering, Taiyuan University of Technology, Taiyuan 030024, China.

Jinwen BaiCollege of Mining Engineering, Taiyuan University of Technology, Taiyuan 030024, China.ORCID 0000-0002-7357-8888

Guorui FengCollege of Mining Engineering, Taiyuan University of Technology, Taiyuan 030024, China.

Xudong ShiCollege of Mining Engineering, Taiyuan University of Technology, Taiyuan 030024, China.

Junbiao MaCollege of Mining Engineering, Taiyuan University of Technology, Taiyuan 030024, China.

Funding

Distinguished Youth Funds of National Natural Science Foundation of China 51925402National Natural Science Foundation of China 52174125National Natural Science Foundation of China 52404091Outstanding Youth Cultivation Project in Shanxi Province 202103021222008Scientific Research Foundation for Advanced Talents of Shanxi Institute of Science and Technology 2024014

6 · The paper itself

Abstract

The mechanical properties of fissured sandstone will deteriorate under water-rock interaction. It is crucial to extract the precursor information of fissured sandstone instability under water-rock interaction. The potential of each acoustic emission (AE) parameter as a precursor for instability in the failure process of fissured sandstone was investigated in this study. An experimental dataset comprising 586 acoustic emission experiments was established, and subsequent classification training and testing were conducted using three machine learning (ML) models: AdaBoost, MLP, and Random Forest (RF). The primary parameters for identifying the instability risk state of fissured sandstone include acoustic emission ringing count, energy (mV·ms), centroid frequency, peak frequency, Rise Angle (RA), Average Frequency (AF),

Indexed as

acoustic emission (AE)fissured sandstoneinstability identificationmachine learningprecursor informationwater–rock interaction

Identifiers

PMID39275662

DOI10.3390/s24175752

PMCPMC11397907

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