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Author：

Shen, X. (Shen, X..) | Chen, Y. (Chen, Y..) | Cao, L. (Cao, L..) | Chen, X. (Chen, X..) | Fu, Y. (Fu, Y..) | Hong, C. (Hong, C..)

Indexed by：

EI Scopus SCIE

Abstract：

The　reasonable　setting　of　the　slurry　pressure　is　very　important　for　the　safety　of　shield　tunnel　construction.　In　view　of　the　common　geological　problems　of　high　water　pressure　and　multiple　fracture　zones　in　water　conveyance　tunnels　in　China,　the　instability　mechanism　of　the　shield　excavation　face　in　these　formations　remains　unclear.　In　this　paper,　based　on　the　Pearl　River　Delta　Water　Resources　Allocation　Project,　various　machine　learning　algorithms　are　introduced,　and　the　predicted　values　of　the　slurry　pressure　obtained　with　different　machine　learning　algorithms　are　evaluated　and　compared.　The　optimal　machine　learning　algorithm　suitable　for　this　project　is　determined.　Then,　using　this　algorithm,　considering　the　influence　of　the　fault　fracture　zone,　the　concept　of　the　formation　influence　coefficient　is　proposed,　which　is　used　as　the　input　of　the　prediction　model.　The　influences　of　the　formation　coefficient　distribution　and　the　influence　width　of　the　fracture　zone　on　the　accuracy　of　slurry　pressure　prediction　are　examined.　Finally,　based　on　this　model,　the　slurry　pressure　of　nearly　100　groups　is　predicted　and　verified.　The　results　show　that　(1)　if　the　influence　of　the　fault　fracture　zone　is　considered　and　a　reasonable　influence　width　of　the　fault　fracture　zone　can　be　determined,　the　mean　absolute　error　(MAE)　of　the　predicted　slurry　pressure　is　much　higher　than　that　of　the　predictions　without　considering　the　fault　fracture　zone,　and　the　prediction　accuracy　can　be　improved　by　approximately　25%;　(2)　when　the　formation　influence　coefficient　is　obtained　via　the　method　of　the　theoretical　distribution　and　the　influence　width　of　the　fault　fracture　zone　is　17　rings,　the　prediction　accuracy　of　the　slurry　pressure　reaches　the　optimal　value;　and　(3)　through　on-site　refined　investigation,　it　can　be　determined　that　the　influence　width　of　the　fault　fracture　zone　is　19　rings.　The　research　results　of　this　paper　can　provide　an　effective　method　for　setting　the　shield　slurry　pressure　under　high-water　pressure　conditions　in　the　fault　fracture　zone.　©　2023　Elsevier　Ltd

Keyword：

Shield tunnel Prediction and analysis Machine learning algorithm Fault fracture zone Slurry pressure

Author Community：

  • [ 1 ] [Shen X.]College of Civil and Transportation Engineering, Shenzhen University, Shenzhen, 518060, China
  • [ 2 ] [Shen X.]Key Laboratory of Coastal Urban Resilient Infrastructures (MOE), Shenzhen University, Shenzhen, 518060, China
  • [ 3 ] [Shen X.]Shenzhen Key Laboratory of Green, Efficient and Intelligent Construction of Underground Metro Station, Guangdong, Shenzhen, 518060, China
  • [ 4 ] [Chen Y.]College of Civil and Transportation Engineering, Shenzhen University, Shenzhen, 518060, China
  • [ 5 ] [Cao L.]Institute of Geotechnical and Underground Engineering, Beijing University of Technology, Beijing, 100124, China
  • [ 6 ] [Chen X.]College of Civil and Transportation Engineering, Shenzhen University, Shenzhen, 518060, China
  • [ 7 ] [Chen X.]Key Laboratory of Coastal Urban Resilient Infrastructures (MOE), Shenzhen University, Shenzhen, 518060, China
  • [ 8 ] [Chen X.]Shenzhen Key Laboratory of Green, Efficient and Intelligent Construction of Underground Metro Station, Guangdong, Shenzhen, 518060, China
  • [ 9 ] [Fu Y.]College of Civil and Transportation Engineering, Shenzhen University, Shenzhen, 518060, China
  • [ 10 ] [Fu Y.]Key Laboratory of Coastal Urban Resilient Infrastructures (MOE), Shenzhen University, Shenzhen, 518060, China
  • [ 11 ] [Fu Y.]Shenzhen Key Laboratory of Green, Efficient and Intelligent Construction of Underground Metro Station, Guangdong, Shenzhen, 518060, China
  • [ 12 ] [Hong C.]College of Civil and Transportation Engineering, Shenzhen University, Shenzhen, 518060, China
  • [ 13 ] [Hong C.]Key Laboratory of Coastal Urban Resilient Infrastructures (MOE), Shenzhen University, Shenzhen, 518060, China
  • [ 14 ] [Hong C.]Shenzhen Key Laboratory of Green, Efficient and Intelligent Construction of Underground Metro Station, Guangdong, Shenzhen, 518060, China

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Source ：

Tunnelling and Underground Space Technology

ISSN： 0886-7798

Year： 2024

Volume： 144

6 . 9 0 0

JCR@2022

Cited Count：

WoS CC Cited Count： 0

SCOPUS Cited Count： 7

ESI Highly Cited Papers on the List： 0 Unfold All

WanFang Cited Count：

Chinese Cited Count：

30 Days PV： 8

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