Prediction of Q-Value by Multi-Variable Regression and Novel Genetic Algorithm Based on the Most Influential Parameters

Document Type : Research Papers

Authors

1 Civil Engineering-Razi University-Kermanshah-Iran

2 Civil Eng.-Razi University-Kermanshah-Iran

3 Razi University/Kermanshah

Abstract

Determination of tunnel support, required for tunnel stability and safety, is an important debate in tunnel engineering field. Q-system classification is a technique used to determine the support system of a tunnel in rock. The problem is that all the required parameters of support system are not accessible. On the other hand, such accesses are very costly and time consuming. Therefore, it is impossible to determine the Q-value in all cases. This paper identifies the most influential parameters of Q-system using SPSS program. Then, it adopts multi-variable regression (MVR) and genetic algorithm (GA) methods to propose a relation for predicting the Q-value using three influential parameters. To this end, 140 experimental data are used. To assess the obtained models, 34 new experimental data, which are not in the primary dataset, are used. The innovation of this paper is that instead of six parameters, the Q-value is determined using three parameters with the highest impact on it instead of six parameters. In this study, the MVR model, with RMSE = 2.68 and correlation coefficient = 0.81 for train data and RMSE = 2.55 and correlation coefficient = 0.80 for test data, showed better performance than GA model, with RMSE = 2.90 and correlation coefficient = 0.82 for train data and RMSE = 2.61 and correlation coefficient = 0.84 for test data.

Keywords

Abdollahzadeh, G.R., Jahani, E. and Kashir, Z. (2017). "Genetic Programming based formulation to predict compressive strength of high strength concrete", Civil Engineering Infrastructures Journal, 50(2), 207-219.

Alemdag, S., Gurocak, Z., Cevik, A., Cabalar, A.F. and Gokceoglu, C. (2016). "Modeling  deformation modulus of a stratified sedimentary rock mass using Neural Network, Fuzzy Inference and Genetic Programming", Engineering Geology, 203, 70-82.

Anbalagana, R., Singhb, B. and Bhargavab, P. (2003). "Half tunnels along hill roads of Himalaya, An innovative approach", Tunnelling and Underground Space Technology, 18, 411-419.

Barton, N. (2002). "Some new Q-value correlations to assist in site characterization and tunnel  design", International Journal of Rock Mechanics and Mining Sciences, 39, 185-216.

Barton, N. and Gammelsaeter, B. (2010). "Application of the Q-system and QTBM prognosis to predict TBM tunnelling potential for the planned Oslo-Ski Rail tunnels", Nordic Rock Mechanics Conference, Kongsberg, Norway.

Barton, N. and Grimstad, E. (2014). "Forty years with the Q-system in Norway and Abroad", FJELLSPRENGNINGSTEKNIKK, NFF, Oslo, 4.1-4.25.

Barton, N. and Grimstad, E. (2014). "Q-system, An illustrated guide following Forty years in  tunnelling", Technical Report, www.nickbarton.com.

Barton, N.R., Lien, R. and Lunde, J. (1974). "Engineering classification of rock masses for the  design of tunnel support", Rock Mechanics, 6(4), 189-239.

Beiki, M., Majdi, A. and Givshad, A. (2013). "Application of genetic programming to predict the uniaxial compressive strength and elastic modulus of carbonate rocks", International Journal of Rock Mechanics and Mining Sciences, 63, 159-169.

Bhandary, R.P., Krishnamoorthy, A. and Rao, A.U. (2019). "Stability analysis of slopes using Finite Element method and Genetic Algorithm", Geotechnical and Geological Engineering, 37, 1877-1889.

Bieniawski, Z.T. (1973). "Engineering classification of jointed rock masses", Transaction of the South African Institution of Civil Engineers, 15, 335-344. 

Chun, B.S., Ryu, W., Sagong, M. and Nam Do, J. (2009). "Indirect estimation of the rock deformation modulus based on polynomial and multiple regression analyses of the RMR system", International Journal of Rock Mechanics and Mining Sciences, 46, 649-658.

Dadkhah, R., Ajalloeian, R. and Hoseeinmizaei, Z. (2010). "Investigation of engineering geology characterization of Khersan 3 dam site", The 1st International Applied Geological Congress, Tabriz, Iran.

Dadkhah, R. and Hoseeinmirzaee, Z. (2014). "Determination strength parameters rock masses Jajarm tunnel based on geotechnical study", Journal of Biodiversity and Environmental Sciences, 4(6), 495- 502.

Dastorani, M.T., Mahjoobi, J., Talebi, A. and Fakhr, F., (2018). "Application of machine learning Aapproaches in rainfall-runoff modelling (Case study: Zayabdeh Rood basin in Iran)", Civil Engineering Infrastructures Journal, 51(2), 293-310.

Ding, S., Xu, L., Su, C. and Jin, F. (2013). "An optimizing method of RBF Neural Network based on Genetic Algorithm", Neural Computing and Applications, 21(2), 333-336.

Erdik, T. and Pektas, A.O. (2019). "Rock slope damage level prediction by using multivariate adaptive regression splines (MARS)", Neural Computing and Applications, 31, 2269-2278.

Fereidooni, D., Khanlari, Gh. and Heidari, M. (2015). "Assessment of a modified rock mass  classification system for rock slop stability analysis in the Q-system", Earth Sciences Research Journal, 19(2), 147-152.

Goel, R.K., Jethwa, J.L. and Paithankar, A.G. (1996). "Correlation between Barton's Q and Bieniawski's RMR, A new approach", International Journal of Rock Mechanics and Mining Sciences, 33(2), 179-181.

Hassan, W.H. (2019). "Application of a Genetic Algorithm for the optimization of a location and inclination angle of a cut-off wall for anisotropic foundations under hydraulic structures", Geotechnical and Geological Engineering, 37, 883-895.

Holland, J. H. (1975). Adaptation in natural and artificial systems, University of Michigan Press, Ann Arbor.

Jalalifar, H., Mojedifar, S., Sahebi, A.A. and Nezamabadipour, H. (2011). "Application of the  adaptive neuro-fuzzy inference system for prediction of a rock engineering classification system", Computers and Geotechnics, 38, 783-790.

Jalalifar, H., Mojedifar, S. and Sahebi, A.A. (2014). "Prediction of rock mass rating using fuzzy logic and multi-variable RMR regression model", International Journal of Mining Science and Technology, 24, 237-244.

Jang, H. and Topal, E. (2013). "Optimizing overbreak prediction based on geological parameters  comparing multiple regression analysis and Artificial Neural Network", Tunnelling and Underground Space Technology, 38, 161-169.

Karimaee Tabarestani, M. and Zarrati, A.R. (2015). "Design of riprap stone around bridge piers using Empircal and Neural Network method", Civil Engineering Infrastructures Journal, 48(1), 1755-188.

Liu, K.Y., Qiao, C.S. and Tian, S.F. (2004). "Design of tunnel shotcrete-bolting support based on a Support Vector Machine", International Journal of rock Mechanics and Mining Sciences, 41(3), 3-9.

Majdi, A. and Beiki, M. (2010). "Evolving neural network using a genetic algorithm for predicting the deformation modulus of rock masses", International Journal of Rock Mechanics and Mining Sciences, 47, 246-253.

Mardia, K.V., Kent. J.T. and Bibby. J.M. (1979). Multivariate analysis, Academic Press,  ISBN 0-12-471252-5.

Makurat, A., Loset, F., Wold Hagen, A., Tunbridge, L., Kveldsvik, V. and Grimstad, E. (2006). "A descriptive rock mechanics model for the 380-500 m level", Norwegian Geotechnical Institute, Report No. R-02-11, ISSN 1402-3091.  

Miyamoto, A. and Motoshita, M. (2015). "Development and practical application of a bridge management System (J-BMS) in Japan", Civil Engineering Infrastructures Journal, 48(1), 189-216.

Park, H., Kim, K. and Kimb, Y. (2015). "Field performance of a genetic algorithm in the settlement prediction of a thick soft clay deposit in the southern part of the Korean peninsula", Engineering Geology, 196, 150-157.

Rezae, A. and Rangbaran, S. (2012). Practical training Genetic Algorithm and Fuzzy Logic in  Matlab, Padideh Publication.

Safarzadeh, A., Zaji, A.M. and Bonakdari, H. (2017). "Comparative assessment of the Hybrid Genetic Algorithm, Artificial Neural Network and Genetic Programming methods for the prediction of longitudinal velocity field around a single straight groyne", Applied Soft Computing, 60, 213-228.

Schwingenschloegl, R. and Lehmann, Ch. (2009). "Swelling rock behaviour in a tunnel: NATM-support vs. Q-support, A comparison", Tunnelling and Underground Space Technology, 24, 356-362.

Terzaghi, K. (1946). "Rock defects and loads on tunnel supports in rock tunneling with steel  supports", Commercial Shearing and Stamping Company, 1, 17-99.

Tzamos, S. and Sofianos, A.I. (2006). "Extending the Q system prediction of support in tunnels  employing fuzzy logic and extra parameters", International Journal of rock Mechanics and Mining Sciences, 43, 938- 949.

Wickham, G.E., Tiedemann, H.R. and Skinner, E.H. (1972). "Support determination based on geologic predictions", North American Rapid Excavation Tunneling Conference, Chicago, pp. 43-64. 

Yagiz, S., Ghasemi, E. and Adoko, A.C. (2018). "Prediction of rock brittleness using Genetic  Algorithm and Particle Swarm Optimization techniques", Geotechnical and Geological Engineering, 36, 3767-3777.

References

Abdollahzadeh, G.R., Jahani, E. and Kashir, Z. (2017). "Genetic Programming based formulation to predict compressive strength of high strength concrete", Civil Engineering Infrastructures Journal, 50(2), 207-219.
Alemdag, S., Gurocak, Z., Cevik, A., Cabalar, A.F. and Gokceoglu, C. (2016). "Modeling  deformation modulus of a stratified sedimentary rock mass using Neural Network, Fuzzy Inference and Genetic Programming", Engineering Geology, 203, 70-82.
Anbalagana, R., Singhb, B. and Bhargavab, P. (2003). "Half tunnels along hill roads of Himalaya, An innovative approach", Tunnelling and Underground Space Technology, 18, 411-419.
Barton, N. (2002). "Some new Q-value correlations to assist in site characterization and tunnel  design", International Journal of Rock Mechanics and Mining Sciences, 39, 185-216.
Barton, N. and Gammelsaeter, B. (2010). "Application of the Q-system and QTBM prognosis to predict TBM tunnelling potential for the planned Oslo-Ski Rail tunnels", Nordic Rock Mechanics Conference, Kongsberg, Norway.
Barton, N. and Grimstad, E. (2014). "Forty years with the Q-system in Norway and Abroad", FJELLSPRENGNINGSTEKNIKK, NFF, Oslo, 4.1-4.25.
Barton, N. and Grimstad, E. (2014). "Q-system, An illustrated guide following Forty years in  tunnelling", Technical Report, www.nickbarton.com.
Barton, N.R., Lien, R. and Lunde, J. (1974). "Engineering classification of rock masses for the  design of tunnel support", Rock Mechanics, 6(4), 189-239.
Beiki, M., Majdi, A. and Givshad, A. (2013). "Application of genetic programming to predict the uniaxial compressive strength and elastic modulus of carbonate rocks", International Journal of Rock Mechanics and Mining Sciences, 63, 159-169.
Bhandary, R.P., Krishnamoorthy, A. and Rao, A.U. (2019). "Stability analysis of slopes using Finite Element method and Genetic Algorithm", Geotechnical and Geological Engineering, 37, 1877-1889.
Bieniawski, Z.T. (1973). "Engineering classification of jointed rock masses", Transaction of the South African Institution of Civil Engineers, 15, 335-344. 
Chun, B.S., Ryu, W., Sagong, M. and Nam Do, J. (2009). "Indirect estimation of the rock deformation modulus based on polynomial and multiple regression analyses of the RMR system", International Journal of Rock Mechanics and Mining Sciences, 46, 649-658.
Dadkhah, R., Ajalloeian, R. and Hoseeinmizaei, Z. (2010). "Investigation of engineering geology characterization of Khersan 3 dam site", The 1st International Applied Geological Congress, Tabriz, Iran.
Dadkhah, R. and Hoseeinmirzaee, Z. (2014). "Determination strength parameters rock masses Jajarm tunnel based on geotechnical study", Journal of Biodiversity and Environmental Sciences, 4(6), 495- 502.
Dastorani, M.T., Mahjoobi, J., Talebi, A. and Fakhr, F., (2018). "Application of machine learning Aapproaches in rainfall-runoff modelling (Case study: Zayabdeh Rood basin in Iran)", Civil Engineering Infrastructures Journal, 51(2), 293-310.
Ding, S., Xu, L., Su, C. and Jin, F. (2013). "An optimizing method of RBF Neural Network based on Genetic Algorithm", Neural Computing and Applications, 21(2), 333-336.
Erdik, T. and Pektas, A.O. (2019). "Rock slope damage level prediction by using multivariate adaptive regression splines (MARS)", Neural Computing and Applications, 31, 2269-2278.
Fereidooni, D., Khanlari, Gh. and Heidari, M. (2015). "Assessment of a modified rock mass  classification system for rock slop stability analysis in the Q-system", Earth Sciences Research Journal, 19(2), 147-152.
Goel, R.K., Jethwa, J.L. and Paithankar, A.G. (1996). "Correlation between Barton's Q and Bieniawski's RMR, A new approach", International Journal of Rock Mechanics and Mining Sciences, 33(2), 179-181.
Hassan, W.H. (2019). "Application of a Genetic Algorithm for the optimization of a location and inclination angle of a cut-off wall for anisotropic foundations under hydraulic structures", Geotechnical and Geological Engineering, 37, 883-895.
Holland, J. H. (1975). Adaptation in natural and artificial systems, University of Michigan Press, Ann Arbor.
Jalalifar, H., Mojedifar, S., Sahebi, A.A. and Nezamabadipour, H. (2011). "Application of the  adaptive neuro-fuzzy inference system for prediction of a rock engineering classification system", Computers and Geotechnics, 38, 783-790.
Jalalifar, H., Mojedifar, S. and Sahebi, A.A. (2014). "Prediction of rock mass rating using fuzzy logic and multi-variable RMR regression model", International Journal of Mining Science and Technology, 24, 237-244.
Jang, H. and Topal, E. (2013). "Optimizing overbreak prediction based on geological parameters  comparing multiple regression analysis and Artificial Neural Network", Tunnelling and Underground Space Technology, 38, 161-169.
Karimaee Tabarestani, M. and Zarrati, A.R. (2015). "Design of riprap stone around bridge piers using Empircal and Neural Network method", Civil Engineering Infrastructures Journal, 48(1), 1755-188.
Liu, K.Y., Qiao, C.S. and Tian, S.F. (2004). "Design of tunnel shotcrete-bolting support based on a Support Vector Machine", International Journal of rock Mechanics and Mining Sciences, 41(3), 3-9.
Majdi, A. and Beiki, M. (2010). "Evolving neural network using a genetic algorithm for predicting the deformation modulus of rock masses", International Journal of Rock Mechanics and Mining Sciences, 47, 246-253.
Mardia, K.V., Kent. J.T. and Bibby. J.M. (1979). Multivariate analysis, Academic Press,  ISBN 0-12-471252-5.
Makurat, A., Loset, F., Wold Hagen, A., Tunbridge, L., Kveldsvik, V. and Grimstad, E. (2006). "A descriptive rock mechanics model for the 380-500 m level", Norwegian Geotechnical Institute, Report No. R-02-11, ISSN 1402-3091.  
Miyamoto, A. and Motoshita, M. (2015). "Development and practical application of a bridge management System (J-BMS) in Japan", Civil Engineering Infrastructures Journal, 48(1), 189-216.
Park, H., Kim, K. and Kimb, Y. (2015). "Field performance of a genetic algorithm in the settlement prediction of a thick soft clay deposit in the southern part of the Korean peninsula", Engineering Geology, 196, 150-157.
Rezae, A. and Rangbaran, S. (2012). Practical training Genetic Algorithm and Fuzzy Logic in  Matlab, Padideh Publication.
Safarzadeh, A., Zaji, A.M. and Bonakdari, H. (2017). "Comparative assessment of the Hybrid Genetic Algorithm, Artificial Neural Network and Genetic Programming methods for the prediction of longitudinal velocity field around a single straight groyne", Applied Soft Computing, 60, 213-228.
Schwingenschloegl, R. and Lehmann, Ch. (2009). "Swelling rock behaviour in a tunnel: NATM-support vs. Q-support, A comparison", Tunnelling and Underground Space Technology, 24, 356-362.
Terzaghi, K. (1946). "Rock defects and loads on tunnel supports in rock tunneling with steel  supports", Commercial Shearing and Stamping Company, 1, 17-99.
Tzamos, S. and Sofianos, A.I. (2006). "Extending the Q system prediction of support in tunnels  employing fuzzy logic and extra parameters", International Journal of rock Mechanics and Mining Sciences, 43, 938- 949.
Wickham, G.E., Tiedemann, H.R. and Skinner, E.H. (1972). "Support determination based on geologic predictions", North American Rapid Excavation Tunneling Conference, Chicago, pp. 43-64. 
Yagiz, S., Ghasemi, E. and Adoko, A.C. (2018). "Prediction of rock brittleness using Genetic  Algorithm and Particle Swarm Optimization techniques", Geotechnical and Geological Engineering, 36, 3767-3777.
Civil Engineering Infrastructures Journal
Volume 54, Issue 2
December 2021
Pages 267-280

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History

  • Receive Date: 05 January 2020
  • Revise Date: 17 April 2020
  • Accept Date: 28 May 2020

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