Prediction model of tunnel boring machine performance by ensemble neural networks - 文章详情页

作者：

Zhao, Z. (Zhao, Z..) | Gong, Q. (Gong, Q..) | Zhang, Y. (Zhang, Y..) (学者：张勇) | Zhao, J. (Zhao, J..) (学者：赵京)

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Scopus

摘要：

The　penetration　rate　of　a　tunnel　boring　machine　(TBM)　depends　on　many　factors　ranging　from　the　machine　design　to　the　geological　properties.　Therefore　it　may　not　be　possible　to　capture　this　complex　relationship　in　an　explicit　mathematical　expression.　In　this　paper,　we　propose　an　ensemble　neural　network　(ENN)　to　predict　TBM　performance.　Based　on　site　data,　a　four-parameter　ENN　model　for　the　prediction　of　the　specific　rock　mass　boreability　index　is　constructed.　Such　a　neural-network-based　model　has　the　advantages　of　taking　into　account　the　uncertainties　embedded　in　the　site　data　and　making　appropriate　inferences　using　very　limited　data　via　the　re-sampling　technique.　The　ENN-based　prediction　model　is　compared　with　a　non-linear　regression　model　derived　from　the　same　four　parameters.　The　ENN　model　outperforms　the　non-linear　regression　model.

关键词：

Ensemble neural network; Specific rock mass boreability index; Tunnel boring machine performance

作者机构：

[ 1 ] [Zhao, Z.]School of Civil and Environmental Engineering, Nanyang Technological University, Nanyang, Singapore
[ 2 ] [Gong, Q.]Ecole Polytechnique Fédérales de Lausanne (EPFL), Rock Mechanics Laboratory, Lausanne, Switzerland
[ 3 ] [Gong, Q.]College of Architecture and Civil Engineering, Beijing University of Technology, Beijing, China
[ 4 ] [Zhang, Y.]School of Civil and Environmental Engineering, Nanyang Technological University, Nanyang, Singapore
[ 5 ] [Zhao, J.]Ecole Polytechnique Fédérales de Lausanne (EPFL), Rock Mechanics Laboratory, Lausanne, Switzerland

通讯作者信息：

[Zhao, Z.]School of Civil and Environmental Engineering, Nanyang Technological University, Nanyang, Singapore

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