Tunneling Paper: Forecasting of TBM advance rate in hard rock condition based on artificial neural network and genetic programming techniques

Forecasting of TBM advance rate in hard rock condition based on artificial neural network and genetic programming techniques

Authors

Jian Zhou
Behnam Yazdani Bejarbaneh
Danial Jahed Armaghani
M. M. Tahir

First Online: 10 December 2019


Abstract

The efficiency of tunnel boring machine (TBM) is regarded as a key factor in successfully undertaking any mechanical tunneling project. In fact, an accurate forecasting of TBM performance, especially in a specified rock mass condition, can minimize capital costs and scheduling for tunnel excavation. This study puts an effort to propose two accurate and practical predictive models of TBM performance via artificial neural network (ANN) and genetic programming (GP) approaches. To set a certain prediction target for the proposed models, the advance rate (AR) of TBM is considered as its performance metric. For modeling purpose, a large experimental database containing 1286 data sets was set up as the result of conducting site investigation operations for a tunneling project in Malaysia, called the Pahang–Selangor Raw Water Transfer Tunnel and performing a number of laboratory tests on the collected rock samples. To design the desired intelligent models of AR based on the training and test patterns, a mix of rock and machine characteristics with the most influence on AR has been used as input parameters, i.e., rock quality designation (RQD), uniaxial compressive strength (UCS), rock mass rating (RMR), Brazilian tensile strength (BTS), thrust force (TF), and revolution per minute (RPM). In addition, a series of statistical indices, such as root mean squared error (RMSE), determination coefficient (R-square), and variance account for (VAF) are utilized to evaluate and compare the prediction precision of the developed models. Based on the simulation results and the computed values of indices, it is observed that the proposed GP model with the training and test RMSE values 0.0427 and 0.0388, respectively, performs noticeably better than the proposed ANN model giving RMSE values 0.0509 and 0.0472 for the training and test sets, respectively. Additionally, a parametric analysis has been conducted on the proposed GP model to further verify its generalization capability. The obtained results demonstrate that this GP-based model could provide a new applicable equation for accuratly predicting TBM performance.

References

Abad SVANK, Tugrul A, Gokceoglu C, Armaghani DJ (2016) Characteristics of weathering zones of granitic rocks in Malaysia for geotechnical engineering design. Eng Geol 200:94–103
Alavi Nezhad Khalil Abad SV, Yilmaz M, Jahed Armaghani D, Tugrul A (2016) Prediction of the durability of limestone aggregates using computational techniques. Neural Comput Appl.  https://doi.org/10.1007/s00521-016-2456-8
Armaghani DJ, Hajihassani M, Sohaei H et al (2015) Neuro-fuzzy technique to predict air-overpressure induced by blasting. Arab J Geosci 8:10937–10950.
Armaghani D, Mohamad E, Hajihassani M (2016) Evaluation and prediction of flyrock resulting from blasting operations using empirical and computational methods. Eng Comput 32:109–121
Armaghani DJ, Mohamad ET, Narayanasamy MS et al (2017) Development of hybrid intelligent models for predicting TBM penetration rate in hard rock condition. Tunn Undergr Sp Technol 63:29–43.
Armaghani DJ, Hasanipanah M, Amnieh HB, Mohamad ET (2018) Feasibility of ICA in approximating ground vibration resulting from mine blasting. Neural Comput Appl 29:457–465
Asadi M, Eftekhari M, Bagheripour MH (2011) Evaluating the strength of intact rocks through genetic programming. Appl Soft Comput 11:1932–1937
Bamford WE (1984) Rock test indices are being successfully correlated with tunnel boring machine performance. In: Fifth Australian Tunnelling Conference: State of the Art in Underground Development and Construction; Preprints of Papers. Institution of Engineers, Australia, p 218
Basheer IA, Hajmeer M (2000) Artificial neural networks: fundamentals, computing, design, and application. J Microbiol Methods 43:3
Baykasoğlu A, Güllü H, Çanakçı H, Özbakır L (2008) Prediction of compressive and tensile strength of limestone via genetic programming. Expert Syst Appl 35:111–123
Beiki, M., Bashari, A. and Majdi, A., 2010. Genetic programming approach for estimating the deformation modulus of rock mass using sensitivity analysis by neural network. International journal of rock mechanics and mining sciences, 47(7), pp.1091-1103.
Bejarbaneh BY, Bejarbaneh EY, Amin MFM et al (2018) Intelligent modelling of sandstone deformation behaviour using fuzzy logic and neural network systems. Bull Eng Geol Environ 77:345–361
Benardos AG, Kaliampakos DC (2004) Modelling TBM performance with artificial neural networks. Tunn Undergr Sp Technol 19:597–605
Bruines P (1998) Neuro-fuzzy modeling of TBM performance with emphasis on the penetration rate. Mem Cent Eng Geol Netherlands, Delft 202
Bejarbaneh EY, Bagheri A, Bejarbaneh BY, Buyamin S, Chegini SN (2019) A new adjusting technique for PID type fuzzy logic controller using PSOSCALF optimization algorithm. Applied Soft Computing 85:105822. https://doi.org/10.1016/j.asoc.2019.105822
Caudill M (1988) Neural networks primer, Part III. AI Expert 3:53–59
Chen H, Asteris PG, Jahed Armaghani D et al (2019) Assessing dynamic conditions of the retaining wall: developing two hybrid intelligent models. Appl Sci 9:1042
Cramer NL (1985) A representation for the adaptive generation of simple sequential programs. Proceedings of the first international conference on genetic algorithms, In, pp 183–187
Dreyfus G (2005) Neural networks: methodology and applications. Springer, Berlin, Heidelberg
Faradonbeh RS, Armaghani DJ, Monjezi M, Mohamad ET (2016a) Genetic programming and gene expression programming for flyrock assessment due to mine blasting. Int J Rock Mech Min Sci 88:254–264
Faradonbeh RS, Jahed Armaghani D, Monjezi M (2016b) Development of a new model for predicting flyrock distance in quarry blasting: a genetic programming technique. Bull Eng Geol Environ 75.
Farmer IW, Glossop NH (1980) Mechanics of disc cutter penetration. Tunnels Tunn 12:22–25
Farrokh E, Rostami J, Laughton C (2012) Study of various models for estimation of penetration rate of hard rock TBMs. Tunn Undergr Sp Technol 30:110–123
Fattahi H (2016) Adaptive neuro fuzzy inference system based on fuzzy c–means clustering algorithm, a technique for estimation of TBM penetration rate. Iran Univ Sci Technol 6:159–171
Ferreira C (2001) Algorithm for solving gene expression programming: a new adaptive problems. Complex Syst 13:87–129
Ferreira C (2006) Gene expression programming: mathematical modeling by an artificial intelligence. Springer
Ghasemi E, Yagiz S, Ataei M (2014) Predicting penetration rate of hard rock tunnel boring machine using fuzzy logic. Bull Eng Geol Environ 73:23–35
Graham PC (1976) Rock exploration for machine manufacturers. Explor rock Eng:173–180
Grima MA, Bruines PA, Verhoef PNW (2000) Modeling tunnel boring machine performance by neuro-fuzzy methods. Tunn Undergr Sp Technol 15:259–269
Hasanipanah M, Jahed Armaghani D, Monjezi M, Shams S (2016) Risk assessment and prediction of rock fragmentation produced by blasting operation: a rock engineering system. Environ Earth Sci 75.
Hornik K, Stinchcombe M, White H (1989) Multilayer feedforward networks are universal approximators. Neural networks 2:359–366
Hrnjica B, Danandeh Mehr A (2018) Optimized genetic programming applications: emerging research and opportunities: emerging research and opportunities. IGI Global, ISBN: 1522560068. 
Jahed Armaghani D, Hajihassani M, Yazdani Bejarbaneh B et al (2014) Indirect measure of shale shear strength parameters by means of rock index tests through an optimized artificial neural network. Meas J Int Meas Confed 55:487–498. 
Karakus M (2011) Function identification for the intrinsic strength and elastic properties of granitic rocks via genetic programming (GP). Comput Geosci 37:1318–1323
Khandelwal M, Marto A, Fatemi SA, et al (2017) Implementing an ANN model optimized by genetic algorithm for estimating cohesion of limestone samples. Eng Comput 1–11
Koopialipoor M, Armaghani DJ, Haghighi M, Ghaleini EN (2017) A neuro-genetic predictive model to approximate overbreak induced by drilling and blasting operation in tunnels. Bull Eng Geol Environ.  https://doi.org/10.1007/s10064-017-1116-2
Koopialipoor M, Nikouei SS, Marto A et al (2018) Predicting tunnel boring machine performance through a new model based on the group method of data handling. Bull Eng Geol Environ 78:3799–3813
Koopialipoor M, Ghaleini EN, Tootoonchi H et al (2019) Developing a new intelligent technique to predict overbreak in tunnels using an artificial bee colony-based ANN. Environ Earth Sci 78:165. 
Koza JR (1992) Genetic programming II, automatic discovery of reusable subprograms. MIT Press,
Li W-X, Dai L-F, Hou X-B, Lei W (2007) Fuzzy genetic programming method for analysis of ground movements due to underground mining. Int J Rock Mech Min Sci 44:954–961
Liao X, Khandelwal M, Yang H et al (2019) Effects of a proper feature selection on prediction and optimization of drilling rate using intelligent techniques. Eng Comput. 
Looney CG (1996) Advances in feedforward neural networks: demystifying knowledge acquiring black boxes. IEEE Trans Knowl Data Eng 8:211–226
Mahdevari S, Shahriar K, Yagiz S, Shirazi MA (2014) A support vector regression model for predicting tunnel boring machine penetration rates. Int J Rock Mech Min Sci 72:214–229
Minh VT, Katushin D, Antonov M, Veinthal R (2017) Regression models and fuzzy logic prediction of TBM penetration rate. Open Eng 7:60–68
Nourani V, Baghanam AH, Adamowski J, Gebremichael M (2013) Using self-organizing maps and wavelet transforms for space–time pre-processing of satellite precipitation and runoff data in neural network based rainfall–runoff modeling. J Hydrol 476:228–243
Ravandi EG, Rahmannejad R, Monfared AEF, Ravandi EG (2013) Application of numerical modeling and genetic programming to estimate rock mass modulus of deformation. Int J Min Sci Technol 23:733–737
Rostami J, Ozdemir L (1993) A new model for performance prediction of hard rock TBMs. In: Proceedings of the rapid excavation and tunneling conference. Society for Mining, Metallogy & Exploration, INC, p 793
Roxborough FF, Phillips HR (1975) Rock excavation by disc cutter. International Journal of Rock Mechanics and Mining Sciences & Geomechanics Abstracts. Elsevier, In, pp 361–366
Safa M, Shariati M, Ibrahim Z et al (2016) Potential of adaptive neuro fuzzy inference system for evaluating the factors affecting steel-concrete composite beam’s shear strength. Steel Compos Struct 21:679–688
Saghatforoush A, Monjezi M, Faradonbeh RS, Armaghani DJ (2016) Combination of neural network and ant colony optimization algorithms for prediction and optimization of flyrock and back-break induced by blasting. Eng Comput 32:255–266
Salimi A, Rostami J, Moormann C, Delisio A (2016) Application of non-linear regression analysis and artificial intelligence algorithms for performance prediction of hard rock TBMs. Tunn Undergr Sp Technol 58:236–246
Sapigni M, Berti M, Bethaz E et al (2002) TBM performance estimation using rock mass classifications. Int J Rock Mech Min Sci 39:771–788
Sato K, Gong F, Itakura K (1991) Prediction of disc cutter performance using a circular rock cutting ring. In: Proceedings 1st international mine mechanization and automation symposium
Shams S, Monjezi M, Majd VJ, Armaghani DJ (2015) Application of fuzzy inference system for prediction of rock fragmentation induced by blasting. Arab J Geosci 8:10819–10832
Shao Z, Armaghani DJ, Bejarbaneh BY et al (2019) Estimating the Friction Angle of Black Shale Core Specimens with Hybrid-ANN Approaches. Measurement.  https://doi.org/10.1016/j.measurement.2019.06.007
Shijing W, Bo Q, Zhibo G (2006) The time and cost prediction of tunnel boring machine in tunnelling. Wuhan Univ J Nat Sci 11:385–388
Shirani Faradonbeh R, Monjezi M, Jahed Armaghani D (2016) Genetic programing and non-linear multiple regression techniques to predict backbreak in blasting operation. Eng Comput 32.  https://doi.org/10.1007/s00366-015-0404-3
Silva S, Almeida J (2003) Dynamic maximum tree depth. In: Genetic and Evolutionary Computation Conference. Springer, pp 1776–1787
Simpson PK (1990) Artificial neural systems: foundations, paradigms, applications, and implementations. Pergamon
Singh R, Kainthola A, Singh TN (2012) Estimation of elastic constant of rocks using an ANFIS approach. Appl Soft Comput 12:40–45
Specht DF (1991) A general regression neural network. IEEE Trans neural networks 2:568–576
Sundaram M (2007) The effects of ground conditions on TBM performance in tunnel excavation–A case history
Sundaram NM, Rafek AG, Komoo I (1998) The influence of rock mass properties in the assessment of TBM performance. In: Proceedings of the 8th IAEG Congress. Vancouver, British Columbia, Canada, pp 3553–3559
Swingler K (1996) Applying neural networks: a practical guide. Academic Press, New York
Tugrul A, GÜRPINAR O (1997) The effect of chemical weathering on the engineering properties of Eocene basalts in northeastern Turkey. Environ Eng Geosci 3:225–234
Ulusay R, Hudson, JAISRM (2007) The complete ISRM suggested methods for rock characterization, testing and monitoring: 1974–2006. Comm Test methods Int Soc Rock Mech Compil arranged by ISRM Turkish Natl Group, Ankara, Turkey 628:
Wang M, Shi X, Zhou J, Qiu X (2018) Multi-planar detection optimization algorithm for the interval charging structure of large-diameter longhole blasting design based on rock fragmentation aspects. Eng Optim 50:2177–2191
Wang M, Shi X, Zhou J (2019) Optimal charge scheme calculation for multiring blasting using modified Harries mathematical model. J Perform Constr Facil 33:4019002
Xu C, Gordan B, Koopialipoor M et al (2019) Improving performance of retaining walls under dynamic conditions developing an optimized ANN based on ant colony optimization technique. IEEE Access 7:94692–94700
Yagiz S (2002) Development of rock fracture and brittleness indices to quantify the effects of rock mass features and toughness in the CSM model basic penetration for hard rock tunneling machine
Yagiz S (2008) Utilizing rock mass properties for predicting TBM performance in hard rock condition. Tunn Undergr Sp Technol 23:326–339
Yagiz S, Karahan H (2011) Prediction of hard rock TBM penetration rate using particle swarm optimization. Int J Rock Mech Min Sci 48:427–433
Yagiz S, Gokceoglu C, Sezer E, Iplikci S (2009) Application of two non-linear prediction tools to the estimation of tunnel boring machine performance. Eng Appl Artif Intell 22:808–814
Yang HQ, Zeng YY, Lan YF, Zhou XP (2014) Analysis of the excavation damaged zone around a tunnel accounting for geostress and unloading. Int J rock Mech Min Sci 69:59–66
Yang H, Wang H, Zhou X (2016) Analysis on the damage behavior of mixed ground during TBM cutting process. Tunn Undergr Sp Technol 57:55–65
Yang H, Liu J, Liu B (2018a) Investigation on the cracking character of jointed rock mass beneath TBM disc cutter. Rock Mech Rock Eng 51:1263–127
Yang HQ, Li Z, Jie TQ, Zhang ZQ (2018b) Effects of joints on the cutting behavior of disc cutter running on the jointed rock mass. Tunn Undergr Sp Technol 81:112–120
Yang H, Hasanipanah M, Tahir MM, Bui DT (2019a) Intelligent prediction of blasting-induced ground vibration using ANFIS optimized by GA and PSO. Nat Resour Res.  https://doi.org/10.1007/s11053-019-09515-3
Yang H, Koopialipoor M, Armaghani DJ et al (2019b) Intelligent design of retaining wall structures under dynamic conditions. STEEL Compos Struct 31:629–640
Yazdani B (2012) Shear strength parameters of shale based on triaxial compression test. Universiti Teknologi Malaysia, Johor Bahru
Yazdani Bejarbaneh B, Jahed Armaghani D, Mohd Amin MF (2015) Strength characterisation of shale using Mohr-Coulomb and Hoek-Brown criteria. Meas J Int Meas Confed. 63:269-281. https://doi.org/10.1016/j.measurement.2014.12.029
Zhao J, Broms BB, Zhou Y, Choa V (1994) A study of the weathering of the Bukit Timah granite part B: field and laboratory investigations. Bull Int Assoc Eng Geol l’Association Int Géologie l’Ingénieur 50:105–111
Zhou J, Li E, Wei H et al (2019a) Random forests and cubist algorithms for predicting shear strengths of rockfill materials. Appl Sci 9:1621
Zhou J, Li E, Wang M, Chen X, Shi X, Jiang L (2019b) Feasibility of Stochastic Gradient Boosting Approach for Evaluating Seismic Liquefaction Potential Based on SPT and CPT Case Histories. Journal of Performance of Constructed Facilities 33 (3):04019024
Zhou J, Li E, Yang S, Wang M, Shi X, Yao S, Mitri HS (2019c). Slope stability prediction for circular mode failure using gradient boosting machine approach based on an updated database of case histories. Safety Science, 118, 505-518.
Zhou J, Li X, Shi X (2012) Long-term prediction model of rockburst in underground openings using heuristic algorithms and support vector machines. Safety Science 50 (4):629-644
Zhou J, Li X, Mitri HS (2016b) Classification of Rockburst in Underground Projects: Comparison of Ten Supervised Learning Methods. Journal of Computing in Civil Engineering 30 (5):04016003
Zhou J, Li X, Mitri HS (2018) Evaluation method of rockburst: State-of-the-art literature review. Tunnelling and Underground Space Technology 81:632-659
Zhou J, Shi X, Li X (2016a) Utilizing gradient boosted machine for the prediction of damage to residential structures owing to blasting vibrations of open pit mining. J Vib Control 22:3986–3997
Zhou XP, Yang HQ (2007) Micromechanical modeling of dynamic compressive responses of mesoscopic heterogenous brittle rock. Theor Appl Fract Mech 48:1–20
Zorlu K, Gokceoglu C, Ocakoglu F et al (2008) Prediction of uniaxial compressive strength of sandstones using petrography-based models. Eng Geol 96:141–

 

 

Article costs 45 Euro from publisher Springer
https://link.springer.com/article/10.1007%2Fs10064-019-01626-8

Null