首页|Machine learning-based classification of rock discontinuity trace:SMOTE oversampling integrated with GBT ensemble learning

Machine learning-based classification of rock discontinuity trace:SMOTE oversampling integrated with GBT ensemble learning

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This paper presents a hybrid ensemble classifier combined synthetic minority oversampling technique(SMOTE),random search(RS)hyper-parameters optimization algorithm and gradient boosting tree(GBT)to achieve efficient and accurate rock trace identification.A thirteen-dimensional database consist-ing of basic,vector,and discontinuity features is established from image samples.All data points are clas-sified as either"trace"or"non-trace"to divide the ultimate results into candidate trace samples.It is found that the SMOTE technology can effectively improve classification performance by recommending an optimized imbalance ratio of 1:5 to 1:4.Then,sixteen classifiers generated from four basic machine learning(ML)models are applied for performance comparison.The results reveal that the proposed RS-SMOTE-GBT classifier outperforms the other fifteen hybrid ML algorithms for both trace and non-trace classifications.Finally,discussions on feature importance,generalization ability and classification error are conducted for the proposed classifier.The experimental results indicate that more critical fea-tures affecting the trace classification are primarily from the discontinuity features.Besides,cleaning up the sedimentary pumice and reducing the area of fractured rock contribute to improving the overall clas-sification performance.The proposed method provides a new alternative approach for the identification of 3D rock trace.

Tunnel faceRock discontinuity traceMachine learningGradient boosting treeGeneralization ability

Jiayao Chen、Hongwei Huang、Anthony G.Cohn、Dongming Zhang、Mingliang Zhou

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Key Laboratory of Geotechnical and Underground Engineering of Ministry of Education,Department of Geotechnical Engineering,Tongji University,Shanghai 200092,China

School of Computing,University of Leeds,LS2 9JT Leeds,United Kingdom

Department of Computer Science and Technology,Tongji University,Shanghai 211985,China

School of Civil Engineering,Shandong University,Jinan 250061,China

Luzhong Institute of Safety,Environmental Protection Engineering and Materials,Qingdao University of Science and Technology,Zibo 255000,China

School of Mechanical and Electrical Engineering,Qingdao University of Science and Technology,Qingdao 260061,China

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Key inno-vation team program of innovation talents promotion plan by MOST of ChinaNatural Science Foundation Committee Program of ChinaScience and Technology Project of Yunnan Provincial Transportation Depart-ment

2016RA40595177847425 of 2018

2022

矿业科学技术学报(英文版)
中国矿业大学

矿业科学技术学报(英文版)

CSTPCDCSCDSCIEI
影响因子：1.222
ISSN：2095-2686
年,卷(期)：2022.32(2)
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