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Optimized ANN model for predicting rock mass quality ahead of tunnel face using measure-while-drilling data

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Abstract

Rock mass quality assessment has a vital influence on the excavation of tunnels and caverns in rock mass. For this purpose, extensive field studies, including records of measure-while-drilling data and rock mass quality scores (RQS) from the observation reports of tunnel faces, have been conducted. In order to predict RQS, three optimized artificial neural network (ANN) models based on genetic algorithm (GA), particle swarm optimization (PSO), and imperialist competition algorithm (ICA) were developed. Six parameters of measure-while-drilling (MWD) data and their corresponding RQS constituted 1270 datasets, which were set as input and output of ANN, respectively. The traditional multiple linear regression (MLR), multiple nonlinear regression (MNR) statistical model, and ANN model were developed as comparative models. Comparison results reveal that PSO-ANN and ICA-ANN models are capable of predicting RQS with higher reliability than the MLR, MNR, ANN, and GA-ANN models. Results indicate that PSO-ANN and ICA-ANN models can be used to predict RQS; however, the PSO-ANN model has better performance.

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References

  • Ahmadi MA, Ebadi M, Shokrollahi A, Majidi SMJ (2013) Evolving artificial neural network and imperialist competitive algorithm for prediction oil flow rate of the reservoir . Appl Soft Comput 13:1085–1098. https://doi.org/10.1016/j.asoc.2012.10.009

    Article  Google Scholar 

  • Akagi W, Sano A, Shinji M, Nishi T, Nakagawa K (2001) A new rock mass classification method at tunnel face for tunnel support system. Doboku Gakkai Ronbunshu 2001:121–134

    Article  Google Scholar 

  • Aoki K, Shirasagi S, Yamamoto T, Inou M, Nishioka K (1999) Examination of the application of drill logging to predict ahead of the tunnel face. In: Proceedings of the 54th Annual Conference of the Japan Society of Civil Engineers, Tokyo, Japan, September 1999, pp 412–413

  • Armaghani DJ, Hasanipanah M, Mahdiyar A, Abd Majid MZ, Bakhshandeh Amnieh H, Tahir MMD (2018) Airblast prediction through a hybrid genetic algorithm-ANN model. Neural Comput Appl 29:619–629. https://doi.org/10.1007/s00521-016-2598-8

    Article  Google Scholar 

  • Armaghani DJ, Koopialipoor M, Marto A, Yagiz S (2019) Application of several optimization techniques for estimating TBM advance rate in granitic rocks. J Rock Mech Geotech Eng 11:779–789. https://doi.org/10.1016/j.jrmge.2019.01.002

    Article  Google Scholar 

  • Atashpaz-Gargari E, Lucas C (2007) Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition. In: 2007 IEEE Congress on Evolutionary Computation, 25–28 Sept 2007, pp 4661–4667. https://doi.org/10.1109/CEC.2007.4425083

  • Back AD, Chen T (2002) Universal approximation of multiple nonlinear operators by neural networks. Neural Comput 14:2561–2566

  • Barton N, Lien R, Lunde J (1974) Engineering classification of rock masses for the design of tunnel support. Rock Mech 6:189–236. https://doi.org/10.1007/bf01239496

    Article  Google Scholar 

  • Bieniawski Z (1973) Engineering classification of jointed rock masses. Civil Engineer in South Africa 15

  • Clerc M, Kennedy J (2002) The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Trans Evol Comput 6:58–73

    Article  Google Scholar 

  • Deere DU (1964) Technical description of rock cores for engineering purpose. Rock Mechanics and Engineering Geology 1:17–22

    Google Scholar 

  • Dybowski R, Gant V, Weller P, Chang R (1996) Prediction of outcome in critically ill patients using artificial neural network synthesised by genetic algorithm. The Lancet 347:1146–1150. https://doi.org/10.1016/S0140-6736(96)90609-1

    Article  Google Scholar 

  • Erharter GH, Marcher T, Reinhold C (2019) Artificial neural network based online rockmass behavior classification of TBM data. In: Information technology in geo-engineering. Springer International Publishing, Cham, pp 178–188

  • Gandomi AH, Yun GJ, Yang X-S, Talatahari S (2013) Chaos-enhanced accelerated particle swarm optimization. Commun Nonlinear Sci Numer Simul 18:327–340. https://doi.org/10.1016/j.cnsns.2012.07.017

    Article  Google Scholar 

  • Gao D (1998) On structures of supervised linear basis function feedforward three-layered neural networks Chinese Journal of Computers 1

  • Gazafroudi AS, Bigdeli N, Ramandi MY, Afshar K (2014) A hybrid model for wind power prediction composed of ANN and imperialist competitive algorithm (ICA). In: 2014 22nd Iranian Conference on Electrical Engineering (ICEE), 20–22 May 2014, pp 562–567. https://doi.org/10.1109/IranianCEE.2014.6999606

  • Han W, Li G, Sun Z, Luan H, Liu C, Wu X (2020) Numerical investigation of a foundation pit supported by a composite soil nailing structure. Symmetry 12:252

    Article  Google Scholar 

  • Hasanipanah M, Jahed Armaghani D, Bakhshandeh Amnieh H, Majid MZA, Tahir MMD (2017) Application of PSO to develop a powerful equation for prediction of flyrock due to blasting. Neural Comput Appl 28:1043–1050. https://doi.org/10.1007/s00521-016-2434-1

    Article  Google Scholar 

  • Hoballah A, Erlich I (2009) PSO-ANN approach for transient stability constrained economic power generation. In: 2009 IEEE Bucharest PowerTech, 28 June–2 July 2009, pp 1–6. https://doi.org/10.1109/PTC.2009.5281926

  • Hoek E, Brown ET (1997) Practical estimates of rock mass strength. Int J Rock Mech Min Sci 34:1165–1186. https://doi.org/10.1016/S1365-1609(97)80069-X

    Article  Google Scholar 

  • Holland JH (1992) Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. MIT Press

  • Hornik K (1991) Approximation capabilities of multilayer feedforward networks. Neural Networks 4:251–257. https://doi.org/10.1016/0893-6080(91)90009-T

    Article  Google Scholar 

  • Hussain S, Mohammad N, Khan M, Rehman ZU, Tahir M (2016) Comparative analysis of rock mass rating prediction using different inductive modeling techniques. International Journal of Mining Engineering Mineral Processing 5:9–15

    Google Scholar 

  • Kanellopoulos I, Wilkinson GG (1997) Strategies and best practice for neural network image classification. Int J Remote Sens 18:711–725

    Article  Google Scholar 

  • Karlaftis A (2018) Classifying rock masses using artificial neural networks. In: Geoecology and computers. Routledge, pp 279–284

  • Kayabasi A (2012) Prediction of pressuremeter modulus and limit pressure of clayey soils by simple and non-linear multiple regression techniques: a case study from Mersin, Turkey. Environ Earth Sci 66:2171–2183

  • Kennedy J, Eberhart R (1995) Particle swarm optimization (PSO). In: Proc. IEEE International Conference on Neural Networks, Perth, pp 1942–1948

  • Kennedy J, Eberhart RC (1997) A discrete binary version of the particle swarm algorithm. In: 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation, 12–15 Oct 1997, vol 4105, pp 4104-4108. https://doi.org/10.1109/ICSMC.1997.637339

  • Khandelwal M, Mahdiyar A, Armaghani DJ, Singh TN, Fahimifar A, Faradonbeh RS (2017) An expert system based on hybrid ICA-ANN technique to estimate macerals contents of Indian coals. Environ Earth Sci 76:399. https://doi.org/10.1007/s12665-017-6726-2

    Article  Google Scholar 

  • Knofczynski GT, Mundfrom D (2008) Sample sizes when using multiple linear regression for prediction. Educ Psychol Meas 68:431–442

  • Lear WE, Dareing DW (1990) Effect of drillstring vibrations on MWD pressure pulse signals. J Energy Res Technol 112:84

  • Leung R, Scheding S (2015) Automated coal seam detection using a modulated specific energy measure in a monitor-while-drilling context. Int J Rock Mech Min Sci 75:196–209. https://doi.org/10.1016/j.ijrmms.2014.10.012

    Article  Google Scholar 

  • Li SC, Wu J, Xu ZH, Li LP (2017) Unascertained measure model of water and mud inrush risk evaluation in karst tunnels and its engineering application. KSCE J Civ Eng 21:1170–1182. https://doi.org/10.1007/s12205-016-1569-z

    Article  Google Scholar 

  • Liu J, Luan H, Zhang Y, Sakaguchi O, Jiang Y (2020) Prediction of unconfined compressive strength ahead of tunnel face using measurement-while-drilling data based on hybrid genetic algorithm. Geotech Eng 22. https://doi.org/10.12989/gae.2020.22.1.000

  • Looney CG (1996) Advances in feedforward neural networks: demystifying knowledge acquiring black boxes. IEEE Transactions on Knowledge Data Engineering, pp 211–226

  • Lowson A, Bieniawski Z (2013) Critical assessment of RMR based tunnel design practices: a practical engineer’s approach. In: Proceedings of the SME, Rapid Excavation and Tunnelling Conference, Washington, DC, pp 23–26

  • Lu J, Liu X (2009) Construction techniques for water and sand gushing section in Xiushan Tunnel on Yuxi-Mengzi railway. Tunnel Construction 3

  • Marto A, Hajihassani M, Jahed Armaghani D, Tonnizam Mohamad E, Makhtar AM (2014) A novel approach for blast-induced flyrock prediction based on imperialist competitive algorithm and artificial neural network. The Scientific World Journal 2014

  • Masahiro N, Koji M, Hiroshi Y, Takuro N, Kazuo N, Koji N (1999) A new proposal of evaluation system for tunnel face based on the analysis of the observation records. Journal of Japan Society of Civil Engineers 623:131–141

    Google Scholar 

  • McCulloch WS, Pitts W (1943) A logical calculus of the ideas immanent in nervous activity. The Bulletin of Mathematical Biophysics 5:115–133. https://doi.org/10.1007/bf02478259

    Article  Google Scholar 

  • Moayedi H, Jahed Armaghani D (2018) Optimizing an ANN model with ICA for estimating bearing capacity of driven pile in cohesionless soil. Eng Comput 34:347–356. https://doi.org/10.1007/s00366-017-0545-7

    Article  Google Scholar 

  • Moayedi H, Mehrabi M, Mosallanezhad M, Rashid ASA, Pradhan B (2019) Modification of landslide susceptibility mapping using optimized PSO-ANN technique. Eng Comput 35:967–984. https://doi.org/10.1007/s00366-018-0644-0

    Article  Google Scholar 

  • Mohamad ET, Hajihassani M, Armaghani DJ, Marto A (2012) Simulation of blasting-induced air overpressure by means of artificial neural networks. Int Rev Modell Simulations 5:2501–2506

    Google Scholar 

  • Momeni E, Nazir R, Jahed Armaghani D, Maizir H (2014) Prediction of pile bearing capacity using a hybrid genetic algorithm-based ANN. Measurement 57:122–131. https://doi.org/10.1016/j.measurement.2014.08.007

    Article  Google Scholar 

  • Momeni E, Nazir R, Armaghani DJ, Maizir H (2015) Application of Artificial Neural Network for predicting shaft and tip resistances of concrete piles. Earth Sci Res J 19:85–93

    Article  Google Scholar 

  • Monjezi M, Ghafurikalajahi M, Bahrami A (2011) Prediction of blast-induced ground vibration using artificial neural networks. Tunn Undergr Space Technol 26:46–50. https://doi.org/10.1016/j.tust.2010.05.002

    Article  Google Scholar 

  • Monjezi M, Hasanipanah M, Khandelwal M (2013) Evaluation and prediction of blast-induced ground vibration at Shur River Dam, Iran, by artificial neural network. Neural Computing Applications and Applied Mathematics 22:1637–1643

    Article  Google Scholar 

  • Nasseri M, Asghari K, Abedini MJ (2008) Optimized scenario for rainfall forecasting using genetic algorithm coupled with artificial neural network. Expert Syst Appl 35:1415–1421. https://doi.org/10.1016/j.eswa.2007.08.033

    Article  Google Scholar 

  • Navarro J, Sanchidrián J, Segarra P, Castedo R, Costamagna E, López L (2018) Detection of potential overbreak zones in tunnel blasting from MWD data. Tunn Undergr Space Technol 82:504–516. https://doi.org/10.1016/j.tust.2018.08.060

    Article  Google Scholar 

  • Nelson MM, Illingworth WT (1991) A practical guide to neural nets

  • Nilsen B (2015) Main challenges for deep subsea tunnels based on norwegian experience. J of Korean Tunn Undergr Sp Assoc 17:563–573. https://doi.org/10.9711/KTAJ.2015.17.5.563

    Article  Google Scholar 

  • Nouiri M, Bekrar A, Jemai A, Niar S, Ammari AC (2018) An effective and distributed particle swarm optimization algorithm for flexible job-shop scheduling problem. J Intell Manuf 29:603–615. https://doi.org/10.1007/s10845-015-1039-3

    Article  Google Scholar 

  • Palmstrom A (2005) Measurements of and correlations between block size and rock quality designation (RQD). Tunn Undergr Space Technol 20:362–377. https://doi.org/10.1016/j.tust.2005.01.005

    Article  Google Scholar 

  • Rahmati A, Faramarzi L, Sanei M (2014) Development of a new method for RMR and Q classification method to optimize support system in tunneling. Frontiers of Structural Civil Engineering 8:448–455. https://doi.org/10.1007/s11709-014-0262-x

    Article  Google Scholar 

  • Rehman H, Naji AM, Kim J-J, Yoo H-K (2018) Empirical evaluation of rock mass rating and tunneling quality index system for tunnel support design. Appl Sci 8:782

    Article  Google Scholar 

  • Rosenblatt F (1958) The perceptron: a probabilistic model for information storage and organization in the brain. Psychol Rev 65:386

    Article  Google Scholar 

  • Shi Y, Eberhart RC (1998) Parameter selection in particle swarm optimization. In: Evolutionary Programming VII. Springer, Berlin Heidelberg, pp 591–600

  • Shin HS, Han KC, Sunwoo C, Choi SO, Choi YK (1999) Collapse of a tunnel in weak rock and the optimal design of the support system. Paper presented at the 9th ISRM Congress, Paris, 1999

  • Sousa LR, Miranda T, Roggenthen W, Sousa RL (2012) Models for geomechanical characterization of the rock mass formations at DUSEL using data mining techniques. Paper presented at the 46th U.S. Rock Mechanics/Geomechanics Symposium, Chicago, Illinois, 2012

  • Swingler K (1996) Applying neural networks: a practical guide. Morgan Kaufmann

  • Vasumathi B, Moorthi S (2012) Implementation of hybrid ANN–PSO algorithm on FPGA for harmonic estimation. Eng Appl Artif Intell 25:476–483. https://doi.org/10.1016/j.engappai.2011.12.005

  • Wang C, Jiang Y, Liu R, Wang C, Zhang Z, Sugimoto S (2020a) Experimental study of the nonlinear flow characteristics of fluid in 3D rough-walled fractures during shear process. Rock Mech Rock Eng 53:2581–2604. https://doi.org/10.1007/s00603-020-02068-5

  • Wang X, Yuan W, Yan Y, Zhang X (2020b) Scale effect of mechanical properties of jointed rock mass: A numerical study based on particle flow code. Geotech Eng 21:259–268

  • Xu J, Wang J, Ma Y (2007) Rock mass quality assessment based on BP artificial neural network (ANN). A case study of borehole BS03 in Jiujing segment of Beishan, Gansu. Uranium Geology 23:243, 249–256

  • Yang X-S (2010) Engineering optimization: an introduction with metaheuristic applications. John Wiley & Sons

  • Yilmaz I (2009) A new testing method for indirect determination of the unconfined compressive strength of rocks. Int J Rock Mech Min Sci 46:1349–1357

    Article  Google Scholar 

  • Yue ZQ, Lee CF, Law KT, Tham LG (2004) Automatic monitoring of rotary-percussive drilling for ground characterization—illustrated by a case example in Hong Kong. Int J Rock Mech Min Sci 41:573–612. https://doi.org/10.1016/j.ijrmms.2003.12.151

    Article  Google Scholar 

  • Yuji W, Tatsuo K, Masaki K, Kenichi H (2006) Solution with modified perceptron to tunnel cutting face evaluation problems. Geoinformatics 17:61–70

    Article  Google Scholar 

  • Zhou H, Hatherly P, Ramos F, Nettleton E (2011) An adaptive data driven model for characterizing rock properties from drilling data. In: 2011 IEEE International Conference on Robotics and Automation, Shanghai, China, May 2011. IEEE, pp 1909–1915

  • Zolfaghari A, Sohrabi Bidar A, Maleki Javan MR, Haftani M, Mehinrad A (2015) Evaluation of rock mass improvement due to cement grouting by Q-system at Bakhtiary dam site. Int J Rock Mech Min Sci 74:38–44. https://doi.org/10.1016/j.ijrmms.2014.12.004

    Article  Google Scholar 

  • Zorlu K, Gokceoglu C, Ocakoglu F, Nefeslioglu HA, Acikalin S (2008) Prediction of uniaxial compressive strength of sandstones using petrography-based models. Eng Geol 96:141–158. https://doi.org/10.1016/j.enggeo.2007.10.009

    Article  Google Scholar 

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Funding

The authors gratefully acknowledge the support from the Konoike Construction Japan in field data collection and data analysis.

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Authors and Affiliations

  1. Graduate School of Engineering, Nagasaki University, 1-14 Bunkyo-machi, Nagasaki, 852-8521, Japan

    Jiankang Liu, Yujing Jiang, Wei Han & Osamu Sakaguchi

  2. Department of Civil Engineering, Konoike Construction Co., Ltd., 3-6-1, Kitakyuhoji-machi, Chuo-ku, Osaka, 541-0057, Japan

    Osamu Sakaguchi

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  1. Jiankang Liu
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Correspondence to Yujing Jiang.

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Liu, J., Jiang, Y., Han, W. et al. Optimized ANN model for predicting rock mass quality ahead of tunnel face using measure-while-drilling data. Bull Eng Geol Environ 80, 2283–2305 (2021). https://doi.org/10.1007/s10064-020-02057-6

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