Deterministic and probabilistic analysis of tunnel face stability using support vector machine

Li，Bin1; Fu，Yong2; Hong，Yi3; Cao，Zijun4

2021

10.12989/gae.2021.25.1.017

Geomechanics and Engineering   影响因子和分区

2005-307X

2092-6219

This paper develops a convenient approach for deterministic and probabilistic evaluations of tunnel face stability using support vector machine classifiers. The proposed method is comprised of two major steps, i.e., construction of the training dataset and determination of instance-based classifiers. In step one, the orthogonal design is utilized to produce representative samples after the ranges and levels of the factors that influence tunnel face stability are specified. The training dataset is then labeled by two-dimensional strength reduction analyses embedded within OptumG2. For any unknown instance, the second step applies the training dataset for classification, which is achieved by an ad hoc Python program. The classification of unknown samples starts with selection of instance-based training samples using the k-nearest neighbors algorithm, followed by the construction of an instance-based SVM-KNN classifier. It eventually provides labels of the unknown instances, avoiding calculate its corresponding performance function. Probabilistic evaluations are performed by Monte Carlo simulation based on the SVM-KNN classifier. The ratio of the number of unstable samples to the total number of simulated samples is computed and is taken as the failure probability, which is validated and compared with the response surface method.

Monte Carlo simulation Strength reduction analysis Support vector machine The k nearest neighbors Tunnel face stability

SCI ; EI

英语

其他

National Natural Science Foundation of China[51608407] ; NRF-NSFC 3rd Joint Research Grant (Earth Science)[41861144022] ; Fundamental Research Funds for the Central Universities[2042019kf1022] ; China Scholarship Council[201706955065]

Engineering

Engineering, Civil ; Engineering, Geological

WOS:000644707300002

TECHNO-PRESS

20211810282285

Boring machines (machine tools) ; Classification (of information) ; Computer software ; Monte Carlo methods ; Nearest neighbor search

Machine Tools, General:603.1 ; Information Theory and Signal Processing:716.1 ; Computer Software, Data Handling and Applications:723 ; Optimization Techniques:921.5 ; Mathematical Statistics:922.2

2-s2.0-85105762085

Scopus

1.School of Transportation,Wuhan University of Technology,Hubei Highway Engineering Research Center,Wuhan, Hubei,1178 Heping Avenue,430063,China
2.Department of Ocean Science and Engineering,Southern University of Science and Technology,Shenzhen,518055,China
3.College of Civil Engineering and Architecture,Zhejiang University,Hang Zhou,China
4.State Key Laboratory of Water Resources and Hydropower Engineering Science,Key Laboratory of Rock Mechanics in Hydraulic Structural Engineering,Ministry of Education,Wuhan University,Wuhan,8 Donghu South Road,China

Li，Bin,Fu，Yong,Hong，Yi,et al. Deterministic and probabilistic analysis of tunnel face stability using support vector machine[J]. Geomechanics and Engineering,2021,25(1):17-30.

Li，Bin,Fu，Yong,Hong，Yi,&Cao，Zijun.(2021).Deterministic and probabilistic analysis of tunnel face stability using support vector machine.Geomechanics and Engineering,25(1),17-30.

Li，Bin,et al."Deterministic and probabilistic analysis of tunnel face stability using support vector machine".Geomechanics and Engineering 25.1(2021):17-30.