A neuro-genetic predictive model to approximate overbreak induced by drilling and blasting operation in tunnels

Mohammadreza Koopialipoor1, Danial Jahed Armaghani2, Mojtaba Haghighi1, Ebrahim Noroozi Ghaleini1
1Faculty of Mining and Metallurgy, Amirkabir University of Technology, Tehran, Iran
2Faculty of Civil and Environmental Engineering, Amirkabir University of Technology, Tehran, Iran

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Armaghani DJ, Momeni E, Abad SVANK, Khandelwal M (2015) Feasibility of ANFIS model for prediction of ground vibrations resulting from quarry blasting. Environ Earth Sci 74:2845–2860. doi: 10.1007/s12665-015-4305-y

Armaghani D, Mohamad E, Hajihassani M (2016a) Evaluation and prediction of flyrock resulting from blasting operations using empirical and computational methods. Eng Comput 32:109–121

Armaghani DJ, Faradonbeh RS, Rezaei H et al (2016b) Settlement prediction of the rock-socketed piles through a new technique based on gene expression programming. Neural Comput Appl. doi: 10.1007/s00521-016-2618-8

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 Space Technol 63:29–43. doi: 10.1016/j.tust.2016.12.009

Chambers LD (1998) Practical handbook of genetic algorithms: complex coding systems. CRC Press, Boca Raton

Chipperfield AJ, Fleming P, Pohlheim H (1994) Genetic algorithm toolbox: for use with MATLAB; User’s Guide (version 1.2). University of Sheffield, Department of Automatic Control and Systems Engineering

Dreyfus G (2005) Neural networks: methodology and applications. Springer, Berlin

Ebrahimi E, Monjezi M, Khalesi MR, Armaghani DJ (2016) Prediction and optimization of back-break and rock fragmentation using an artificial neural network and a bee colony algorithm. Bull Eng Geol Environ 75:27–36

Garrett JH (1994) Where and why artificial neural networks are applicable in civil engineering. J Comput Civil Eng 8:129–130

Ghoraba S, Monjezi M, Talebi N et al (2016) Estimation of ground vibration produced by blasting operations through intelligent and empirical models. Environ Earth Sci. doi: 10.1007/s12665-016-5961-2

Goh ATC (2000) Search for critical slip circle using genetic algorithms. Civ Eng Syst 17:181–211

Goh ATC, Zhang W (2012) Reliability assessment of stability of underground rock caverns. Int J Rock Mech Min Sci 55:157–163

Haghighi M (2015) Investigation of critical parameters on overbreak in tunneling by intelligent networks. Amirkabir University of Technology, Iran

Hecht-Nielsen R (1987) Kolmogorov’s mapping neural network existence theorem. In: Proceedings of the international conference on neural networks. IEEE Press, New York, pp 11–13

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

Hornik K, Stinchcombe M, White H (1989) Multilayer feedforward networks are universal approximators. Neural Netw 2:359–366

Hush DR (1989) Classification with neural networks: a performance analysis. In: Proceedings of the IEEE international conference on systems engineering. pp 277–280

Ibarra JA, Maerz NH, Franklin JA (1996) Overbreak and underbreak in underground openings part 2: causes and implications. Geotech Geol Eng 14:325–340

Jahed Armaghani D, Hasanipanah M, Mahdiyar A et al (2016) Airblast prediction through a hybrid genetic algorithm-ANN model. Neural Comput Appl. doi: 10.1007/s00521-016-2598-8

Jang H, Topal E (2013) Optimizing overbreak prediction based on geological parameters comparing multiple regression analysis and artificial neural network. Tunn Undergr Space Technol 38:161–169

Kaastra I, Boyd M (1996) Designing a neural network for forecasting financial and economic time series. Neurocomputing 10:215–236

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

Khandelwal M, Armaghani DJ (2016) Prediction of drillability of rocks with strength properties using a hybrid GA-ANN technique. Geotech Geol Eng 34:605–620. doi: 10.1007/s10706-015-9970-9

Khandelwal M, Monjezi M (2013) Prediction of backbreak in open-pit blasting operations using the machine learning method. Rock Mech Rock Eng 46:389–396

Lee Y, Oh S-H, Kim MW (1991) The effect of initial weights on premature saturation in back-propagation learning. In: Neural Networks, 1991, IJCNN-91-Seattle international joint conference on. IEEE, pp 765–770

Majdi A, Beiki M (2010) Evolving neural network using a genetic algorithm for predicting the deformation modulus of rock masses. Int J Rock Mech Min Sci 47:246–253

Masters T (1993) Practical neural network recipes in C++. Morgan Kaufmann, Burlington

Mohamad ET, Faradonbeh RS, Armaghani DJ et al. (2016) An optimized ANN model based on genetic algorithm for predicting ripping production. Neural Comput Appl 1–14. doi: 10.1007/s00521-016-2359-8

Momeni E, Nazir R, Armaghani DJ, Maizir H (2014) Prediction of pile bearing capacity using a hybrid genetic algorithm-based ANN. Measurement 57:122–131

Monjezi M, Dehghani H (2008) Evaluation of effect of blasting pattern parameters on back break using neural networks. Int J Rock Mech Min Sci 45:1446–1453

Monjezi M, Khoshalan HA, Varjani AY (2012) Prediction of flyrock and backbreak in open pit blasting operation: a neuro-genetic approach. Arab J Geosci 5:441–448

Monjezi M, Ahmadi Z, Varjani AY, Khandelwal M (2013) Backbreak prediction in the Chadormalu iron mine using artificial neural network. Neural Comput Appl 23:1101–1107

Monjezi M, Rizi SMH, Majd VJ, Khandelwal M (2014) Artificial neural network as a tool for backbreak prediction. Geotech Geol Eng 32:21–30

Paola JD (1994) Neural network classification of multispectral imagery. M.Sc. thesis, The University of Arizona, USA

Ripley BD (1993) Statistical aspects of neural networks. Netw Chaos Stat probab Asp 50:40–123

Saemi M, Ahmadi M, Varjani AY (2007) Design of neural networks using genetic algorithm for the permeability estimation of the reservoir. J Pet Sci Eng 59:97–105

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

Simpson PK (1990) Artificial neural systems: foundation, paradigms, applications and implementations. Pergamon, New York

Singh TN, Verma AK (2012) Comparative analysis of intelligent algorithms to correlate strength and petrographic properties of some schistose rocks. Eng Comput 28:1–12

Singh SP, Xavier P (2005) Causes, impact and control of overbreak in underground excavations. Tunn Undergr Space Technol 20:63–71

Singh J, Verma AK, Banka H et al (2016) A study of soft computing models for prediction of longitudinal wave velocity. Arab J Geosci 9:1–11

Swingler K (1996) Applying neural networks: a practical guide. Academic Press, New York

Tonnizam Mohamad E, Hajihassani M, Jahed Armaghani D, Marto A (2012) Simulation of blasting-induced air overpressure by means of artificial neural networks. Int Rev Model Simul 5(6):2501–2506

Verma AK, Singh TN (2013) A neuro-fuzzy approach for prediction of longitudinal wave velocity. Neural Comput Appl 22:1685–1693

Wang C (1994) A theory of generalization in learning machines with neural applications. Ph.D. thesis, The University of Pennsylvania, USA

Wang X, Tang Z, Tamura H et al (2004) An improved backpropagation algorithm to avoid the local minima problem. Neurocomputing 56:455–460

Zhang W, Goh ATC (2016) Multivariate adaptive regression splines and neural network models for prediction of pile drivability. Geosci Front 7:45–52

Zhu W (2004) Stability analysis and modelling of underground excavations in fractured rocks. Elsevier Geo-Engineering Book Series. Elsevier Science and Technology, Amsterdam

Zorlu K, Gokceoglu C, Ocakoglu F et al (2008) Prediction of uniaxial compressive strength of sandstones using petrography-based models. Eng Geol 96:141–158