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Developing a new intelligent technique to predict overbreak in tunnels using an artificial bee colony-based ANN

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Abstract

The drilling and blasting technique is among the common techniques for excavating tunnels with different shapes and sizes. Nevertheless, due to the dynamic energy involved, the rock mass around the excavation zone experiences damage and reduction in stiffness and strength. One of the most common and important issues that occurs during the tunneling process is the overbreak which is defined as the surplus drilled section of the tunnel. It seems that prediction of overbreak before blasting operations is necessary to minimize the possible damages. This paper develops a new hybrid model, namely, an artificial bee colony (ABC)–artificial neural network (ANN) to predict overbreak. Considering the most important parameters on overbreak, many ABC–ANN models were constructed based on their effective parameters. A pre-developed ANN model was also developed for comparison. In order to evaluate the obtained results of this study, a new system, i.e., the color intensity rating (CIR), was introduced and established to select the best ABC–ANN and ANN models. As a result, the ABC–ANN receives a high level of accuracy in predicting overbreak induced by drilling and blasting. The coefficients of determination (R2) for the ANN and ABC–ANN are 0.9121 and 0.9428, respectively, for training datasets. This revealed that the ABC–ANN model (as a new model in the field of this study) is the best one among the models developed in this study.

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References

  • Ahmad A, Razali SFM, Mohamed ZS, El-shafie A (2016) The application of artificial bee colony and gravitational search algorithm in reservoir optimization. Water Resour Manag 30:2497–2516

    Article  Google Scholar 

  • 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. https://doi.org/10.1016/j.tust.2016.12.009

    Article  Google Scholar 

  • Bhandari S (1997) Engineering rock blasting operations. A A Balkema 388:388

    Google Scholar 

  • de Oliveira IMS, Schirru R, de Medeiros J (2009) On the performance of an artificial bee colony optimization algorithm applied to the accident diagnosis in a pwr nuclear power plant. In: 2009 International nuclear Atlantic conference (INAC 2009)

  • 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

    Article  Google Scholar 

  • Engelbrecht AP (2007) Computational intelligence: an introduction. Wiley, Hoboken

    Book  Google Scholar 

  • Esmaeili M, Osanloo M, Rashidinejad F et al (2014) Multiple regression, ANN and ANFIS models for prediction of backbreak in the open pit blasting. Eng Comput 30:549–558

    Article  Google Scholar 

  • Fausett L, Fausett L (1994) Fundamentals of neural networks: architectures, algorithms, and applications. Prentice-Hall, Upper Saddle River

    Google Scholar 

  • Garrett JH (1994) Where and why artificial neural networks are applicable in civil engineering. J Comput Civil Eng 8(2):129–130

    Article  Google Scholar 

  • Gates WCB, Ortiz LT, Florez RM (2005) Analysis of rockfall and blasting backbreak problems, US 550, Molas Pass, CO. In: Alaska Rocks 2005, the 40th US symposium on rock mechanics (USRMS). American Rock Mechanics Association

  • Ghaleini EN, Koopialipoor M, Momenzadeh M, Sarafraz ME, Mohamad ET, Gordan B (2018) A combination of artificial bee colony and neural network for approximating the safety factor of retaining walls. Eng Comput. https://doi.org/10.1007/s00366-018-0625-3

    Article  Google Scholar 

  • Gordan B, Jahed Armaghani D, Hajihassani M, Monjezi M (2016) Prediction of seismic slope stability through combination of particle swarm optimization and neural network. Eng Comput. https://doi.org/10.1007/s00366-015-0400-7

    Article  Google Scholar 

  • Gordan B, Koopialipoor M, Clementking A, Tootoonchi H, Mohamad ET (2018) Estimating and optimizing safety factors of retaining wall through neural network and bee colony techniques. Eng Comput. https://doi.org/10.1007/s00366-018-0642-2

    Article  Google Scholar 

  • Grima MA, Bruines PA, Verhoef PNW (2000) Modeling tunnel boring machine performance by neuro-fuzzy methods. Tunn Undergr Sp Technol 15:259–269

    Article  Google Scholar 

  • Hajihassani M, Jahed Armaghani D, Monjezi M et al (2015) Blast-induced air and ground vibration prediction: a particle swarm optimization-based artificial neural network approach. Environ Earth Sci 74:2799–2817. https://doi.org/10.1007/s12665-015-4274-1

    Article  Google Scholar 

  • Hasanipanah M, Armaghani DJ, Amnieh HB, Koopialipoor M, Arab H (2018) A risk-based technique to analyze flyrock results through rock engineering system. Geotech Geol Eng 36(4):2247–2260. https://doi.org/10.1007/s10706-018-0459-1

    Article  Google Scholar 

  • Haykin S, Network N (2004) A comprehensive foundation. Neural Netw 2:41

    Google Scholar 

  • Hecht-Nielsen R (1989) Kolmogorov’s mapping neural network existence theorem. In: Proceedings of the international joint conference in neural networks, pp 11–14

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Irani R, Nasimi R (2011) Application of artificial bee colony-based neural network in bottom hole pressure prediction in underbalanced drilling. J Pet Sci Eng 78:6–12

    Article  Google Scholar 

  • Jahed Armaghani D, Hajihassani M, Monjezi M et al (2015) Application of two intelligent systems in predicting environmental impacts of quarry blasting. Arab J Geosci 8:9647–9665. https://doi.org/10.1007/s12517-015-1908-2

    Article  Google Scholar 

  • Jahed Armaghani D, Tonnizam Mohamad E, Hajihassani M et al (2016) Evaluation and prediction of flyrock resulting from blasting operations using empirical and computational methods. Eng Comput. https://doi.org/10.1007/s00366-015-0402-5

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  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 

  • Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. Technical report-tr06, Erciyes University, Engineering Faculty, Computer Engineering Department

  • Karaboga D, Akay B (2007) Artificial bee colony (ABC) algorithm on training artificial neural networks. In: Signal processing and communications applications, 2007. SIU 2007. IEEE 15th. IEEE, pp 1–4

  • Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Glob Optim 39:459–471

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Koopialipoor M, Armaghani DJ, Hedayat A et al (2018a) Applying various hybrid intelligent systems to evaluate and predict slope stability under static and dynamic conditions. Soft Comput. https://doi.org/10.1007/s00500-018-3253-3

    Article  Google Scholar 

  • Koopialipoor M, Fallah A, Armaghani DJ et al (2018b) Three hybrid intelligent models in estimating flyrock distance resulting from blasting. Eng Comput. https://doi.org/10.1007/s00366-018-0596-4

    Article  Google Scholar 

  • Koopialipoor M, Ghaleini EN, Haghighi M, Kanagarajan S, Maarefvand P, Mohamad ET (2018c) Overbreak prediction and optimization in tunnel using neural network and bee colony techniques. Eng Comput. https://doi.org/10.1007/s00366-018-0658-7

    Article  Google Scholar 

  • Koopialipoor M, Nikouei SS, Marto A, Fahimifar A, Armaghani DJ, Mohamad ET (2018d) Predicting tunnel boring machine performance through a new model based on the group method of data handling. Bull Eng Geol Environ. https://doi.org/10.1007/s10064-018-1349-8

    Article  Google Scholar 

  • Lundborg N (1974) The hazards of flyrock in rock blasting. Swedish Detonic Research Foundation, Reports DS 12, Stockholm

  • Maerz NH, Ibarra JA, Franklin JA (1996) Overbreak and under-break in underground openings Part 1: measurement using the light sectioning method and digital image processing. Geotech Geol Eng 14:307–323

    Article  Google Scholar 

  • Mandal SK, Singh MM (2009) Evaluating extent and causes of overbreak in tunnels. Tunn Undergr Sp Technol 24:22–36

    Article  Google Scholar 

  • 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. Sci World J. https://doi.org/10.1155/2014/643715

    Article  Google Scholar 

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

    Google Scholar 

  • McCulloch WS, Pitts W (1943) A logical calculus of the ideas immanent in nervous activity. Bull Math Biophys 5:115–133

    Article  Google Scholar 

  • Momeni E, Jahed Armaghani D, Hajihassani M, Mohd Amin MF (2015) Prediction of uniaxial compressive strength of rock samples using hybrid particle swarm optimization-based artificial neural networks. Meas J Int Meas Confed. https://doi.org/10.1016/j.measurement.2014.09.075

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Nozohour-leilabady B, Fazelabdolabadi B (2016) On the application of artificial bee colony (ABC) algorithm for optimization of well placements in fractured reservoirs; efficiency comparison with the particle swarm optimization (PSO) methodology. Petroleum 2:79–89

    Article  Google Scholar 

  • Ocak I, Seker SE (2013) Calculation of surface settlements caused by EPBM tunneling using artificial neural network, SVM, and Gaussian processes. Environ Earth Sci 70:1263–1276

    Article  Google Scholar 

  • Paola JD (1994) Neural network classification of multispectral imagery. Master Tezi, Univ Arizona, USA

  • Raina AK, Haldar A, Chakraborty AK et al (2004) Human response to blast-induced vibration and air-overpressure: an Indian scenario. Bull Eng Geol Environ 63:209–214

    Article  Google Scholar 

  • Raina AK, Murthy V, Soni AK (2014) Flyrock in bench blasting: a comprehensive review. Bull Eng Geol Environ 73:1199–1209

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Rodriguez FJ, García-Martínez C, Blum C, Lozano M (2012) An artificial bee colony algorithm for the unrelated parallel machines scheduling problem. In: International conference on parallel problem solving from nature. Springer, pp 143–152

  • Roth J (1979) A model for the determination of flyrock range as a function of shot conditions. US Bureau of Mines contract J0387242. Manag Sci Assoc Los Altos

  • 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

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Wang C (1994) A theory of generalization in learning machines with neural network applications. Doctoral Dissertation, University of Pennsylvania, Philadelphia, PA, USA

  • Zhang C, Ouyang D, Ning J (2010) An artificial bee colony approach for clustering. Expert Syst Appl 37:4761–4767

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

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Acknowledgements

The authors would like to extend their appreciation to the manager, engineers and personnel of Forum708 (Otagh-MEH), for providing the needed information and facilities that made this research possible. Additionally, the authors would like to express their sincere appreciation to reviewers because of their valuable comments that increased the quality of our paper.

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Authors and Affiliations

  1. Faculty of Civil and Environmental Engineering, Amirkabir University of Technology, 15914, Tehran, Iran

    Mohammadreza Koopialipoor, Hossein Tootoonchi & Danial Jahed Armaghani

  2. Faculty of Mining and Metallurgy, Amirkabir University of Technology, Tehran, Iran

    Ebrahim Noroozi Ghaleini & Mojtaba Haghighi

  3. Faculty of Civil and Environmental Engineering, Colorado School of Mines, Golden, CO, 80401, USA

    Ahmadreza Hedayat

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  1. Mohammadreza Koopialipoor
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  2. Ebrahim Noroozi Ghaleini
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  4. Danial Jahed Armaghani
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  5. Mojtaba Haghighi
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  6. Ahmadreza Hedayat
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Correspondence to Mohammadreza Koopialipoor.

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Koopialipoor, M., Ghaleini, E.N., Tootoonchi, H. et al. Developing a new intelligent technique to predict overbreak in tunnels using an artificial bee colony-based ANN. Environ Earth Sci 78, 165 (2019). https://doi.org/10.1007/s12665-019-8163-x

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  • DOI: https://doi.org/10.1007/s12665-019-8163-x

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