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Engineering with Computers
Published in: Engineering with Computers 2/2018

22-07-2017 | Original Article

Optimization of flyrock and rock fragmentation in the Tajareh limestone mine using metaheuristics method of firefly algorithm

Authors: Parvin Faraji Asl, Masoud Monjezi, Jafar Khademi Hamidi, Danial Jahed Armaghani

Published in: Engineering with Computers | Issue 2/2018

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Abstract

A successful and favorable explosion not only causes a proper rock fragmentation, but also decreases the unfavorable and unwanted environmental issues caused by explosion such as ground vibration, flyrock, air-overpressure and back-break. Therefore, anticipation and optimization of these issues produced by blasting operations is significant. In this study, an attempt has been made to design the effective factors on Tajareh limestone mine explosion [i.e., burden, blast-hole, spacing, hole length, sub-drilling, stemming, powder factor, charge in each delay and Geological Strength Index (GSI)] in order to reduce improper fragmentation and flyrock. Since the experimental methods are not suitable in terms of accuracy, using artificial neural network (ANN) and firefly algorithms, flyrock and rock fragmentation were predicted and optimized, respectively. After collecting data and selecting the most effective parameters on flyrock and rock fragmentation, an ANN model was developed, and then its results were called by firefly algorithm for optimizing process. ANN results according to coefficient of determination (R 2) and root mean square error (RMSE) were obtained as 0.94 and 0.1, 0.93 and 0.09, respectively, for fragmentation and flyrock. The outcome results of modeling and optimization showed a decrease of 42.9 and 32.9% in results of flyrock and rock fragmentation, respectively. In addition, results of optimization process were obtained as: 2 m of burden, 2.9 m of spacing, 7.5 m of hole length, 0.7 m of sub-drilling, 1.9 m of obstruction length, 0.69 kg/m3 of powder factor, 1443 kg of charge in each delay and 55.5 of GSI. Based on the obtained results of sensitivity analysis, it was found that GSI and burden receive the highest influence values on both flyrock and rock fragmentation.
previous article Parametric design velocity computation for CAD-based design optimization using adjoint methods
next article Evaluation of effect of rock mass properties on fragmentation using robust techniques

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Literature
1.
Jahed Armaghani D, Hajihassani M, Mohamad ET, Marto A, Noorani SA (2013) Blasting-induced flyrock and ground vibration prediction through an expert artificial neural network based on particle swarm optimization. Arab J Geosci. doi:10.​1007/​s12517-013-1174-0
2.
Khandelwal M, Monjezi M (2013) Prediction of backbreak in open-pit blasting operations using the machine learning method. Rock Mech Rock Eng 46(2):389–396CrossRef
3.
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(3):441–448CrossRef
4.
Khandelwal M, Monjezi M (2013) Prediction of flyrock in open pit blasting operation using machine learning method. Int J Min Sci Technol 23(2013):313–316
5.
Görgülü K, Arpaz E, Demirci A, Koçaslan A, Dilmac MK, Yüksek AG (2013) Investigation of blast-induced ground vibrations in the Tülü boron open pit mine. Bull Eng Geol Environ 72(3–4):555–564CrossRef
6.
7.
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 2014 (Article ID 643715)
8.
Hajihassani M, Jahed Armaghani D, Monjezi M, Mohamad ET, Marto A (2015) Blast-induced air and ground vibration prediction: a particle swarm optimization-based artificial neural network approach. Environ Earth Sci. doi:10.​1007/​s12665-015-4274-1
9.
Institute of Makers of Explosives (IME) (1997) Glossary of commercial explosive industry terms, vol 12. Safety Publication, Institute of Makers of Explosives, Washington DC
10.
Bhandari S (1997) Engineering rock blasting operations. Taylor & Francis, Boca Raton
11.
Michaux S, Djordjevic N (2005) Influence of explosive energy on the strength of the rock fragments and SAG mill throughput. Min Eng 18(4):439–448CrossRef
12.
Monjezi M, Rezaei M, Yazdian Varjani A (2009) Prediction of rock fragmentation due to blasting in Gol-E-Gohar iron mine using fuzzy logic. Int J Rock Mech Min Sci 46(8):1273–1280CrossRef
13.
Zheming X, Mohanty B, Heping Z (2007) Numerical investigation of blasting-induced crack initiation and propagation in rocks. Int J Rock Mech Min Sci 44:412–424CrossRef
14.
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
15.
Tonnizam Mohamad E, Jahed Armaghani D, Momeni E, Khalil Alavi Nezhad, Abad SV (2014) Prediction of the unconfined compressive strength of soft rocks: a PSO-based ANN approach. Bull Eng Geol Environ. doi:10.​1007/​s10064-014-0638-0
16.
Jahed Armaghani D, Hajihassani M, Mohamad ET, Marto A, Noorani SA (2013) Blasting-induced flyrock and ground vibration prediction through an expert artificial neural network based on particle swarm optimization. Arab J Geosci. doi:10.​1007/​s12517-013-1174-0
17.
Saghatforoush A, Monjezi M, Shirani Faradonbeh R, Jahed Armaghani D (2015) Combination of neural network and ant colony optimization algorithms for prediction and optimization of flyrock and back-break induced by blasting. Eng Comput. doi:10.​1007/​s00366-015-0415-0
18.
Singh TN, Kanchan R, Saigal K, Verma AK (2004) Prediction of p-wave velocity and anisotropic property of rock using artificial neural network technique. J Sci Ind Res 63(1):32–38
19.
Singh TN, Verma AK, Singh V, Sahu A (2005) Slake durability study of shaly rock and its predictions. Environ Geol 47(2):246–253CrossRef
20.
Verma AK, Singh TN, Chauhan NK, Sarkar K (2016) A hybrid FEM-ANN approach for slope instability prediction. J Inst Eng India Ser A 97(3):171–1801CrossRef
21.
22.
23.
24.
Verma AK, Singh TN, Maheshwar S (2014) Comparative study of intelligent prediction models for pressure wave velocity. J Geosci Geomat 2(3):130–138
25.
26.
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 Env. doi:10.​1007/​s10064-017-1116-2
27.
Armaghani DJ, Hajihassani M, Monjezi M, Mohamad ET, Marto A, Moghaddam MR (2015) Application of two intelligent systems in predicting environmental impacts of quarry blasting. Arab J Geosci 8(11):9647–9665CrossRef
28.
Bahrami A, Monjezi M, Goshtasbi K, Ghazvinian A (2011) Prediction of rock fragmentation due to blasting using artificial neural network. Eng Comput 27(2):177–181CrossRef
29.
Sayadi A, Monjezi M, Talebi N, Khandelwal M (2013) A comparative study on the application of various artificial neural networks to simultaneous prediction of rock fragmentation and backbreak. J Rock Mech Geotech Eng 5(4):318–324CrossRef
30.
Ebrahimi E, Monjezi M, Khalesi MR, Jahed Armaghani D (2015) Prediction and optimization of backbreak and rock fragmentation using an artificial neural network and a bee colony algorithm. Bull Eng Geol Environ. doi:10.​1007/​s10064-015-0720-2
31.
Rezaei M, Monjezi M, Yazdian Varjani A (2011) Development of a fuzzy model to predict flyrock in surface mining. Saf Sci 49:298–305CrossRef
32.
Monjezi M, Khoshalan HA, Varjani AY (2011) Optimization of open pit blast parameters using genetic algorithm. Int J Rock Mech Min Sci 48:864–869CrossRef
33.
Gustafsson R (1973) Swedish blasting technique and mining. SPI, Gothenburg
34.
Ghasemi E, Amini H, Ataei M, Khalokakaei R (2014) Application of artificial intelligence techniques for predicting the flyrock distance caused by blasting operation. Arab J Geosci 7:193–202. doi:10.​1007/​s12517-012-0703-6 CrossRef
35.
Trivedi R, Singh TN, Raina A (2014) Prediction of blast-induced flyrock in Indian limestone mines using neural networks. J Rock Mech Geotech Eng 6(5):447–454CrossRef
36.
Monjezi M, Bahrami A, Yazdian Varjani A (2010) Simultaneous prediction of fragmentation and flyrock in blasting operation using artificial neural networks. Int J Rock Mech Min Sci 47(3):476–480CrossRef
37.
Hoek E, Brown ET (1997) Practical estimates of rock mass strength. Int J Rock Mech Min Sci 34(8):1165–1186CrossRef
38.
Singh TN, Singh R, Singh B, Sharma LK, Singh R, Ansari MK (2016) Investigations and stability analyses of Malin village landslide of Pune district, Maharashtra, India. Nat Hazards 81:2019–2030CrossRef
39.
Sharma LK, Umrao RK, Singh R, Ahmad M, Singh TN (2017) Stability investigation of hill cut soil slopes along national highway 222 at Malshej Ghat, Maharashtra. J Geol Soc India 89(2):165–174CrossRef
40.
Sharma LK, Umrao RK, Singh R, Ahmad M, Singh TN (2017) Geotechnical characterization of road cut hill slope forming unconsolidated geo-materials: a case study. Geotech Geol Eng 35:503–515. doi:10.​1007/​s10706-016-0093-8 CrossRef
41.
Hunter GC, McDermott C, Miles NJ, Singh A, Scoble MJ (1990) Review of image analysis techniques for measuring blast fragmentation. Min Sci Technol 11:19–36CrossRef
42.
43.
Simpson PK (1990) Artificial neural system-foundation, paradigm, application and implementations. Pergamon Press, New York
44.
Meulenkamp F, Grima MA (1999) Application of neural networks for the prediction of the unconfined compressive strength (UCS) from Equotip hardness. Int J Rock Mech Min Sci 36:29–39CrossRef
45.
Tawadrous AS, Katsabanis PD (2007) Prediction of surface crown pillar stability using artificial neural networks. Int J Numer Anal Met 31:917–931CrossRefMATH
46.
Dreyfus G (2005) Neural networks: methodology and application, 2nd edn. Springer, BerlinMATH
47.
Yang XS (2008) Firefly algorithm, nature-inspired metaheuristic algorithm. Luniver Press, pp 79–90
48.
Yang XS (2009) Firefly algorithms for multimodal optimization. In: Stochastic algorithms. Foundations and applications, vol 5792. Lecture notes in computer sciences. SAGA, Cambridge, pp 169–178
49.
Yang X-S (2010) Firefly algorithm, stochastic test functions and design optimisation. Int J Bio Inspir Comput 2:78–84CrossRef
50.
Zang H, Zhang S, Hapeshi K (2010) A review of nature-inspired algorithms. J Bionic Eng 7:232–237CrossRef
51.
Yang XS (2013) Optimization and metaheuristic algorithms in engineering. In: Yang X-S (ed) Metaheuristic in water geotechnical and transport engineering. Elsevier, Waltham, pp 1–23
52.
Fister I, Fister I Jr, Yang X-S, Brest J (2013) A comprehensive review of firefly algorithms. Contents lists available at ScienceDirect swarm and evolutionary computation, vol 13, pp 34–46
53.
Swingler K (1996) Applying neural networks: a practical guide. Academic, New York
54.
Tonnizam Mohamad E, Faradonbeh RS, Armaghani DJ, Monjezi M, Majid MZA (2016) An optimized ANN model based on genetic algorithm for predicting ripping production. Neural Comput Appl. doi:10.​1007/​s00521-016-2359-8
55.
Armaghani DJ, Faradonbeh RS, Momeni E, Fahimifar A, Tahir MM (2017) Performance prediction of tunnel boring machine through developing a gene expression programming equation. Eng Comput. doi:10.​1007/​s00366-017-0526-x
56.
Yong HJ, Chung IL (2004) Influence of geological conditions on the powder factor for tunnel blasting. Int J Rock Mech Min Sci 41:533–538CrossRef
Metadata
Title
Optimization of flyrock and rock fragmentation in the Tajareh limestone mine using metaheuristics method of firefly algorithm
Authors
Parvin Faraji Asl
Masoud Monjezi
Jafar Khademi Hamidi
Danial Jahed Armaghani
Publication date
22-07-2017
Publisher
Springer London
Published in
Engineering with Computers / Issue 2/2018
Print ISSN: 0177-0667
Electronic ISSN: 1435-5663
DOI
https://doi.org/10.1007/s00366-017-0535-9

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