Discussion on article ‘Influence of Rock Brittleness on TBM Penetration Rate in Singapore Granite’ by Q.M. Gong and J. Zhao [Tunnelling and Underground Space Technology, in Press]

Tunnelling and Underground Space Technology ◽

10.1016/j.tust.2007.03.001 ◽

2008 ◽

Vol 23 (2) ◽

pp. 215-216 ◽

Author(s):

R.M. Goktan

Keyword(s):

Penetration Rate ◽

Underground Space ◽

Space Technology ◽

Rock Brittleness ◽

Tbm Penetration Rate ◽

Article Influence

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Discussion on the article “Influence of rock brittleness on TBM penetration rate in Singapore granite” by Q.M. Gong and J. Zhao [Tunnelling and Underground Space Technology 22 (2007) 317–324] and “Response by the authors to R.M. Goktan discussion to the paper “Influence of rock brittleness on TBM penetration rate in Singapore granite” by Q.M. Gong and J. Zhao [Tunnelling and Underground Space Technology 23 (2008) 217–218]

Tunnelling and Underground Space Technology ◽

10.1016/j.tust.2008.05.002 ◽

2009 ◽

Vol 24 (1) ◽

pp. 115-116

Author(s):

S. Yagiz

Keyword(s):

Penetration Rate ◽

Underground Space ◽

Space Technology ◽

Rock Brittleness ◽

Tbm Penetration Rate ◽

Article Influence

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Response by the authors to R.M. Goktan discussion to the paper: Q.M. Gong and J. Zhao (2007). Influence of rock brittleness on TBM penetration rate in Singapore granite, Tunnelling and Underground Space Technology, Vol. 22, pp. 317–324

Tunnelling and Underground Space Technology ◽

10.1016/j.tust.2007.04.013 ◽

2008 ◽

Vol 23 (2) ◽

pp. 217-218

Author(s):

Q.M. Gong ◽

J. Zhao

Keyword(s):

Penetration Rate ◽

Underground Space ◽

Space Technology ◽

Rock Brittleness ◽

Tbm Penetration Rate

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Influence of rock brittleness on TBM penetration rate in Singapore granite

Tunnelling and Underground Space Technology ◽

10.1016/j.tust.2006.07.004 ◽

2007 ◽

Vol 22 (3) ◽

pp. 317-324 ◽

Author(s):

Q.M. Gong ◽

J. Zhao

Keyword(s):

Penetration Rate ◽

Rock Brittleness ◽

Tbm Penetration Rate

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Setting up simple estimating equations of TBM penetration rate using rock mass classification parameters

Tunnelling and Underground Space Technology ◽

10.1016/j.tust.2021.104065 ◽

2021 ◽

Vol 115 ◽

pp. 104065

Author(s):

Hongyi Xu ◽

Qiuming Gong ◽

Jianwei Lu ◽

Lijun Yin ◽

Fengwei Yang

Keyword(s):

Rock Mass ◽

Penetration Rate ◽

Estimating Equations ◽

Rock Mass Classification ◽

Mass Classification ◽

Tbm Penetration Rate

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Development of a rock mass characteristics model for TBM penetration rate prediction

International Journal of Rock Mechanics and Mining Sciences ◽

10.1016/j.ijrmms.2008.03.003 ◽

2009 ◽

Vol 46 (1) ◽

pp. 8-18 ◽

Author(s):

Q.M. Gong ◽

J. Zhao

Keyword(s):

Rock Mass ◽

Penetration Rate ◽

Rate Prediction ◽

Tbm Penetration Rate

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Reviewers for tunnelling and underground space technology 2004

Tunnelling and Underground Space Technology ◽

10.1016/j.tust.2004.08.002 ◽

2005 ◽

Vol 20 (1) ◽

pp. 105

Keyword(s):

Underground Space ◽

Space Technology

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Prediction of TBM Penetration Rate Using Fuzzy Logic, Particle Swarm Optimization and Harmony Search Algorithm

Geotechnical and Geological Engineering ◽

10.1007/s10706-021-01982-x ◽

2021 ◽

Author(s):

Alireza Afradi ◽

Arash Ebrahimabadi ◽

Tahereh Hallajian

Keyword(s):

Fuzzy Logic ◽

Particle Swarm Optimization ◽

Penetration Rate ◽

Search Algorithm ◽

Harmony Search ◽

Particle Swarm ◽

Harmony Search Algorithm ◽

Swarm Optimization ◽

Tbm Penetration Rate

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Discussion on the paper entitled “Quantitative assessments of the correlations between rock mass rating (RMR) and geological strength index (GSI)” by Qi Zhang, Xianbin Huang, Hehua Zhu and Jianchun Li, Published in Tunnelling and Underground Space Technology (2019) 83:73–81

Tunnelling and Underground Space Technology ◽

10.1016/j.tust.2020.103488 ◽

2021 ◽

Vol 107 ◽

pp. 103488

Author(s):

Yanhui Song

Keyword(s):

Rock Mass ◽

Geological Strength Index ◽

Underground Space ◽

Rock Mass Rating ◽

Space Technology ◽

Strength Index ◽

Quantitative Assessments

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Prediction of TBM penetration rate from brittleness indexes using multiple regression analysis

Modeling Earth Systems and Environment ◽

10.1007/s40808-018-0432-2 ◽

2018 ◽

Vol 4 (1) ◽

pp. 383-394 ◽

Author(s):

A. Jamshidi

Keyword(s):

Regression Analysis ◽

Multiple Regression Analysis ◽

Multiple Regression ◽

Penetration Rate ◽

Tbm Penetration Rate

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Comparison of artificial neural networks (ANN), support vector machine (SVM) and gene expression programming (GEP) approaches for predicting TBM penetration rate

SN Applied Sciences ◽

10.1007/s42452-020-03767-y ◽

2020 ◽

Vol 2 (12) ◽

Author(s):

Alireza Afradi ◽

Arash Ebrahimabadi

Keyword(s):

Gene Expression ◽

Neural Networks ◽

Support Vector Machine ◽

Artificial Neural Networks ◽

Penetration Rate ◽

Gene Expression Programming ◽

Support Vector ◽

Artificial Neural ◽

Tbm Penetration Rate

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