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Erschienen in: Neural Computing and Applications 9/2018

14.09.2016 | Original Article

Airblast prediction through a hybrid genetic algorithm-ANN model

verfasst von: Danial Jahed Armaghani, Mahdi Hasanipanah, Amir Mahdiyar, Muhd Zaimi Abd Majid, Hassan Bakhshandeh Amnieh, Mahmood M. D. Tahir

Erschienen in: Neural Computing and Applications | Ausgabe 9/2018

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Abstract

Air overpressure is one of the most undesirable destructive effects induced by blasting operation. Hence, a precise prediction of AOp has vital importance to minimize or reduce the environmental effects. This paper presents the development of two artificial intelligence techniques, namely artificial neural network (ANN) and ANN based on genetic algorithm (GA) for prediction of AOp. For this purpose, a database was compiled from 97 blasting events in a granite quarry in Penang, Malaysia. The values of maximum charge per delay and the distance from the blast-face were set as model inputs to predict AOp. To verify the quality and reliability of the ANN and GA-ANN models, several statistical functions, i.e., root means square error (RMSE), coefficient of determination (R 2) and variance account for (VAF) were calculated. Based on the obtained results, the GA-ANN model is found to be better than ANN model in estimating AOp induced by blasting. Considering only testing datasets, values of 0.965, 0.857, 0.77 and 0.82 for R 2, 96.380, 84.257, 70.07 and 78.06 for VAF, and 0.049, 0.117, 8.62 and 6.54 for RMSE were obtained for GA-ANN, ANN, USBM and MLR models, respectively, which prove superiority of the GA-ANN in AOp prediction. It can be concluded that GA-ANN model can perform better compared to other implemented models in predicting AOp.

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Literatur
1.
Zurück zum Zitat Khandelwal M, Singh TN (2007) Evaluation of blast-induced ground vibration predictors. Soil Dyn Earthq Eng 27:116–125CrossRef Khandelwal M, Singh TN (2007) Evaluation of blast-induced ground vibration predictors. Soil Dyn Earthq Eng 27:116–125CrossRef
2.
Zurück zum Zitat Dindarloo SR (2015) Prediction of blast-induced ground vibrations via genetic programming. Int J Min Sci Technol 25:1011–1015CrossRef Dindarloo SR (2015) Prediction of blast-induced ground vibrations via genetic programming. Int J Min Sci Technol 25:1011–1015CrossRef
3.
Zurück zum Zitat Shirani Faradonbeh R, Jahed Armaghani D, Abd Majid MZ, Tahir MMD, Ramesh Murlidhar B, Monjezi M, Wong HM (2016) Prediction of ground vibration due to quarry blasting based on gene expression programming: a new model for peak particle velocity prediction. Int J Environ Sci Technol. doi:10.1007/s13762-016-0979-2 Shirani Faradonbeh R, Jahed Armaghani D, Abd Majid MZ, Tahir MMD, Ramesh Murlidhar B, Monjezi M, Wong HM (2016) Prediction of ground vibration due to quarry blasting based on gene expression programming: a new model for peak particle velocity prediction. Int J Environ Sci Technol. doi:10.​1007/​s13762-016-0979-2
4.
Zurück zum Zitat Singh PK, Sinha A (2013) Rock fragmentation by blasting, Fragblast 10. Taylor & Francis Group, London, p 427 Singh PK, Sinha A (2013) Rock fragmentation by blasting, Fragblast 10. Taylor & Francis Group, London, p 427
5.
Zurück zum Zitat Khandelwal M, Singh TN (2005) Prediction of blast induced air overpressure in opencast mine. Noise Vib Control Worldw 36:7–16CrossRef Khandelwal M, Singh TN (2005) Prediction of blast induced air overpressure in opencast mine. Noise Vib Control Worldw 36:7–16CrossRef
6.
Zurück zum Zitat Khandelwal M, Kankar PK (2011) Prediction of blast-induced air overpressure using support vector machine. Arab J Geosci 4:427–433CrossRef Khandelwal M, Kankar PK (2011) Prediction of blast-induced air overpressure using support vector machine. Arab J Geosci 4:427–433CrossRef
7.
Zurück zum Zitat Dindarloo SR (2015) Peak particle velocity prediction using support vector machines: a surface blasting case study. J South Afr Inst Min Metall 115:637–643CrossRef Dindarloo SR (2015) Peak particle velocity prediction using support vector machines: a surface blasting case study. J South Afr Inst Min Metall 115:637–643CrossRef
8.
Zurück zum Zitat Amiri M, Bakhshandeh Amnieh H, Hasanipanah M, Mohammad Khanli L (2016) A new combination of artificial neural network and K-nearest neighbors models to predict blast-induced ground vibration and air-overpressure. Eng Comput. doi:10.1007/s00366-016-0442-5 Amiri M, Bakhshandeh Amnieh H, Hasanipanah M, Mohammad Khanli L (2016) A new combination of artificial neural network and K-nearest neighbors models to predict blast-induced ground vibration and air-overpressure. Eng Comput. doi:10.​1007/​s00366-016-0442-5
9.
Zurück zum Zitat Siskind DE, Stachura VJ, Stagg MS, Koop JW (1980) Structure response and damage produced by airblast from surface mining. Report of investigations, vol 8485. United States Bureau of Mines, Washington, DC Siskind DE, Stachura VJ, Stagg MS, Koop JW (1980) Structure response and damage produced by airblast from surface mining. Report of investigations, vol 8485. United States Bureau of Mines, Washington, DC
10.
Zurück zum Zitat Hustrulid WA (1999) Blasting principles for open pit mining: general design concepts. Balkema, Amsterdam Hustrulid WA (1999) Blasting principles for open pit mining: general design concepts. Balkema, Amsterdam
11.
Zurück zum Zitat Kuzu C, Fisne A, Ercelebi SG (2009) Operational and geological parameters in the assessing blast induced airblast-overpressure in quarries. Appl Acoust 70:404–411CrossRef Kuzu C, Fisne A, Ercelebi SG (2009) Operational and geological parameters in the assessing blast induced airblast-overpressure in quarries. Appl Acoust 70:404–411CrossRef
12.
Zurück zum Zitat Jahed Armaghani D, Hajihassani M, Sohaei H, Mohamad ET, Marto A, Motaghedi H, Moghaddam MR (2015) Neuro-fuzzy technique to predict air-overpressure induced by blasting. Arab J Geosci 8(12):10937–10950CrossRef Jahed Armaghani D, Hajihassani M, Sohaei H, Mohamad ET, Marto A, Motaghedi H, Moghaddam MR (2015) Neuro-fuzzy technique to predict air-overpressure induced by blasting. Arab J Geosci 8(12):10937–10950CrossRef
13.
Zurück zum Zitat Jahed Armaghani D, Hasanipanah M, Mohamad ET (2016) A combination of the ICA-ANN model to predict air-overpressure resulting from blasting. Eng Comput 32(1):155–171CrossRef Jahed Armaghani D, Hasanipanah M, Mohamad ET (2016) A combination of the ICA-ANN model to predict air-overpressure resulting from blasting. Eng Comput 32(1):155–171CrossRef
14.
Zurück zum Zitat Mohamad ET, Jahed Armaghani D, Hasanipanah M, Ramesh Murlidhar B, Asmawisham Alel MN (2016) Estimation of air-overpressure produced by blasting operation through a neuro-genetic technique. Environ Earth Sci 75:174. doi:10.1007/s12665-015-4983-5 CrossRef Mohamad ET, Jahed Armaghani D, Hasanipanah M, Ramesh Murlidhar B, Asmawisham Alel MN (2016) Estimation of air-overpressure produced by blasting operation through a neuro-genetic technique. Environ Earth Sci 75:174. doi:10.​1007/​s12665-015-4983-5 CrossRef
15.
Zurück zum Zitat Verma AK, Singh TN (2009) A Neuro-Genetic approach for prediction of compressional wave velocity of rock and its sensitivity analysis. Int J Earth Sci Eng 2(2):81–94 Verma AK, Singh TN (2009) A Neuro-Genetic approach for prediction of compressional wave velocity of rock and its sensitivity analysis. Int J Earth Sci Eng 2(2):81–94
16.
Zurück zum Zitat Verma AK, Singh TN, Monjezi M (2010) Intelligent prediction of heating value of coal. Iran J Earth Sci 2:32–38 Verma AK, Singh TN, Monjezi M (2010) Intelligent prediction of heating value of coal. Iran J Earth Sci 2:32–38
17.
Zurück zum Zitat Singh TN, Verma AK (2010) Sensitivity of total charge and maximum charge per delay on ground vibration. Geomat Nat Hazards Risk 1(3):259–272CrossRef Singh TN, Verma AK (2010) Sensitivity of total charge and maximum charge per delay on ground vibration. Geomat Nat Hazards Risk 1(3):259–272CrossRef
18.
Zurück zum Zitat Singh R, Vishal V, Singh TN (2012) Soft computing method for assessment of compressional wave velocity. Sci Iran 19(4):1018–1024CrossRef Singh R, Vishal V, Singh TN (2012) Soft computing method for assessment of compressional wave velocity. Sci Iran 19(4):1018–1024CrossRef
19.
Zurück zum Zitat Verma AK, Singh TN (2012) Comparative analysis of intelligent algorithms to correlate strength and petrographic properties of some schistose rocks. Eng Comput 28:1–12CrossRef Verma AK, Singh TN (2012) Comparative analysis of intelligent algorithms to correlate strength and petrographic properties of some schistose rocks. Eng Comput 28:1–12CrossRef
20.
Zurück zum Zitat Singh R, Vishal V, Singh TN, Ranjith PG (2013) A comparative study of generalized regression neural network approach and adaptive neuro-fuzzy inference systems for prediction of unconfined compressive strength of rocks. Neural Comput Appl 23(2):499–506CrossRef Singh R, Vishal V, Singh TN, Ranjith PG (2013) A comparative study of generalized regression neural network approach and adaptive neuro-fuzzy inference systems for prediction of unconfined compressive strength of rocks. Neural Comput Appl 23(2):499–506CrossRef
21.
Zurück zum Zitat Sawmliana C, Roy PP, Singh RK, Singh TN (2007) Blast induced air overpressure and its prediction using artificial neural network. Min Technol 116(2):41–48CrossRef Sawmliana C, Roy PP, Singh RK, Singh TN (2007) Blast induced air overpressure and its prediction using artificial neural network. Min Technol 116(2):41–48CrossRef
22.
Zurück zum Zitat Mohamed MT (2011) Performance of fuzzy logic and artificial neural network in prediction of ground and air vibrations. Int J Rock Mech Min Sci 48:845–851CrossRef Mohamed MT (2011) Performance of fuzzy logic and artificial neural network in prediction of ground and air vibrations. Int J Rock Mech Min Sci 48:845–851CrossRef
23.
Zurück zum Zitat Hasanipanah M, Jahed Armaghani D, Khamesi H, Bakhshandeh Amnieh H, Ghoraba S (2015) Several nonlinear models in estimating air-overpressure resulting from mine blasting. Eng Comput. doi:10.1007/s00366-015-0425-y Hasanipanah M, Jahed Armaghani D, Khamesi H, Bakhshandeh Amnieh H, Ghoraba S (2015) Several nonlinear models in estimating air-overpressure resulting from mine blasting. Eng Comput. doi:10.​1007/​s00366-015-0425-y
24.
Zurück zum Zitat Jadav K, Panchal M (2012) Optimizing weights of artificial neural networks using genetic algorithms. Int J Adv Res Comput Sci Electron Eng 1:47–51 Jadav K, Panchal M (2012) Optimizing weights of artificial neural networks using genetic algorithms. Int J Adv Res Comput Sci Electron Eng 1:47–51
25.
Zurück zum Zitat Momeni E, Nazir R, Jahed Armaghani D, Maizir H (2014) Prediction of pile bearing capacity using a hybrid genetic algorithm-based ANN. Measurement 57:122–131CrossRef Momeni E, Nazir R, Jahed Armaghani D, Maizir H (2014) Prediction of pile bearing capacity using a hybrid genetic algorithm-based ANN. Measurement 57:122–131CrossRef
26.
Zurück zum Zitat Garrett J (1994) Where and why artificial neural networks are applicable in civil engineering. J Comput Civ Eng 8:129–130CrossRef Garrett J (1994) Where and why artificial neural networks are applicable in civil engineering. J Comput Civ Eng 8:129–130CrossRef
27.
Zurück zum Zitat Simpson P (1990) Artificial neural system: foundation, paradigms, applications and implementations. Pergamon, New York Simpson P (1990) Artificial neural system: foundation, paradigms, applications and implementations. Pergamon, New York
28.
Zurück zum Zitat Dreyfus G (2005) Neural networks: methodology and application. Springer, BerlinMATH Dreyfus G (2005) Neural networks: methodology and application. Springer, BerlinMATH
29.
Zurück zum Zitat Holland J (1975) Adaptation in natural and artificial systems. The University of Michigan Press, Ann Arbor Holland J (1975) Adaptation in natural and artificial systems. The University of Michigan Press, Ann Arbor
30.
Zurück zum Zitat Chipperfield A, Fleming P, Pohlheim H (2006) Genetic algorithm toolbox for use with MATLAB User’s guide, version 1.2. University of Sheffield, Sheffield Chipperfield A, Fleming P, Pohlheim H (2006) Genetic algorithm toolbox for use with MATLAB User’s guide, version 1.2. University of Sheffield, Sheffield
31.
Zurück zum Zitat Simpson AR, Dandy GC, Murphy LJ (1994) Genetic algorithms compared to other techniques for pipe optimization. J Water Res PL-ASCE 120:423–443CrossRef Simpson AR, Dandy GC, Murphy LJ (1994) Genetic algorithms compared to other techniques for pipe optimization. J Water Res PL-ASCE 120:423–443CrossRef
32.
Zurück zum Zitat Lee Y, Oh S-H, Kim MW (1991) The effect of initial weights on premature saturation in back-propagation learning. In: Proceedings of the Seattle international joint conference on neural networks (IJCNN-91), vol 1. IEEE, pp 765–770 Lee Y, Oh S-H, Kim MW (1991) The effect of initial weights on premature saturation in back-propagation learning. In: Proceedings of the Seattle international joint conference on neural networks (IJCNN-91), vol 1. IEEE, pp 765–770
33.
Zurück zum Zitat 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 47:246–253CrossRef 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 47:246–253CrossRef
34.
Zurück zum Zitat TingXiang L, ShuWen Z, QuanYuan W et al. (2012) Research of agricultural land classification and evaluation based on genetic algorithm optimized neural network model. In: Wu Y (ed) Software engineering and knowledge engineering: theory and practice. Springer, Berlin, pp 465–471CrossRef TingXiang L, ShuWen Z, QuanYuan W et al. (2012) Research of agricultural land classification and evaluation based on genetic algorithm optimized neural network model. In: Wu Y (ed) Software engineering and knowledge engineering: theory and practice. Springer, Berlin, pp 465–471CrossRef
35.
Zurück zum Zitat Rashidian V, Hassanlourad M (2013) Predicting the shear behavior of cemented and uncemented carbonate sands using a genetic algorithm-based artificial neural network. Geotech Geol Eng 2:1–18 Rashidian V, Hassanlourad M (2013) Predicting the shear behavior of cemented and uncemented carbonate sands using a genetic algorithm-based artificial neural network. Geotech Geol Eng 2:1–18
36.
Zurück zum Zitat Chambers LD (2010) Practical handbook of genetic algorithms: complex coding systems. CRC Press, Boca Raton Chambers LD (2010) Practical handbook of genetic algorithms: complex coding systems. CRC Press, Boca Raton
37.
Zurück zum Zitat Rajasekaran S, Vijayalakshmi Pai GA (2007) Neural networks, fuzzy logic, and genetic algorithms, synthesis and applications. Prentice-Hall of India, New Delhi Rajasekaran S, Vijayalakshmi Pai GA (2007) Neural networks, fuzzy logic, and genetic algorithms, synthesis and applications. Prentice-Hall of India, New Delhi
38.
Zurück zum Zitat Hagan MT, Menhaj MB (1994) Training feed forward networks with the Marquardt algorithm. IEEE Trans Neural Networks 5:861–867CrossRef Hagan MT, Menhaj MB (1994) Training feed forward networks with the Marquardt algorithm. IEEE Trans Neural Networks 5:861–867CrossRef
39.
Zurück zum Zitat 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 59:97–105CrossRef 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 59:97–105CrossRef
40.
Zurück zum Zitat Hopler RB (1998) Blasters’ handbook. International Society of Explosives Engineers, Cleveland, OH Hopler RB (1998) Blasters’ handbook. International Society of Explosives Engineers, Cleveland, OH
41.
Zurück zum Zitat Jahed Armaghani D, 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. doi:10.1007/s12517-015-1908-2 Jahed Armaghani D, 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. doi:10.​1007/​s12517-015-1908-2
42.
Zurück zum Zitat Khamesi H, Torabi S, Mirzaei-Nasirabad H, Ghadiri Z (2015) Improving the performance of intelligent back analysis for tunneling using optimized fuzzy systems: case study of the Karaj Subway Line 2 in Iran. J Comput Civ Eng 29(6):05014010CrossRef Khamesi H, Torabi S, Mirzaei-Nasirabad H, Ghadiri Z (2015) Improving the performance of intelligent back analysis for tunneling using optimized fuzzy systems: case study of the Karaj Subway Line 2 in Iran. J Comput Civ Eng 29(6):05014010CrossRef
43.
Zurück zum Zitat Swingler K (1996) Applying neural networks: a practical guide. Academic Press, New York Swingler K (1996) Applying neural networks: a practical guide. Academic Press, New York
44.
Zurück zum Zitat Looney CG (1996) Advances in feed-forward neural networks: demystifying knowledge acquiring black boxes. IEEE Trans Knowl Data Eng 8(2):211–226CrossRef Looney CG (1996) Advances in feed-forward neural networks: demystifying knowledge acquiring black boxes. IEEE Trans Knowl Data Eng 8(2):211–226CrossRef
45.
Zurück zum Zitat Nelson M, Illingworth WT (1990) A practical guide to neural nets. Addison-Wesley, Reading MAMATH Nelson M, Illingworth WT (1990) A practical guide to neural nets. Addison-Wesley, Reading MAMATH
46.
Zurück zum Zitat Hush DR (1989) Classification with neural networks: a performance analysis. In: Proceedings of the IEEE international conference on systems engineering, Dayton, pp 277–280 Hush DR (1989) Classification with neural networks: a performance analysis. In: Proceedings of the IEEE international conference on systems engineering, Dayton, pp 277–280
47.
Zurück zum Zitat Maulenkamp 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 Maulenkamp 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
48.
Zurück zum Zitat Hornik K, Stinchcombe M, White H (1989) Multilayer feedforward networks are universal approximators. Neural Netw 2:359–366CrossRefMATH Hornik K, Stinchcombe M, White H (1989) Multilayer feedforward networks are universal approximators. Neural Netw 2:359–366CrossRefMATH
49.
Zurück zum Zitat Sonmez H, Gokceoglu C, Nefeslioglu HA, Kayabasi A (2006) Estimation of rock modulus: for intact rocks with an artificial neural network and for rock masses with a new empirical equation. Int J Rock Mech Min Sci 43:224–235CrossRef Sonmez H, Gokceoglu C, Nefeslioglu HA, Kayabasi A (2006) Estimation of rock modulus: for intact rocks with an artificial neural network and for rock masses with a new empirical equation. Int J Rock Mech Min Sci 43:224–235CrossRef
50.
Zurück zum Zitat Hecht-Nielsen R (1987) Kolmogorov’s mapping neural network existence theorem. In: Proceedings of the first IEEE international conference on neural networks, San Diego, pp 11–14 Hecht-Nielsen R (1987) Kolmogorov’s mapping neural network existence theorem. In: Proceedings of the first IEEE international conference on neural networks, San Diego, pp 11–14
51.
Zurück zum Zitat Ripley BD (1993) Statistical aspects of neural networks. In: Barndoff-Neilsen OE, Jensen JL, Kendall WS (eds) Networks and chaos-statistical and probabilistic aspects. Chapman & Hall, London, pp 40–123CrossRef Ripley BD (1993) Statistical aspects of neural networks. In: Barndoff-Neilsen OE, Jensen JL, Kendall WS (eds) Networks and chaos-statistical and probabilistic aspects. Chapman & Hall, London, pp 40–123CrossRef
52.
Zurück zum Zitat Paola JD (1994) Neural network classification of multispectral imagery, M.Sc. thesis. The University of Arizona Paola JD (1994) Neural network classification of multispectral imagery, M.Sc. thesis. The University of Arizona
53.
Zurück zum Zitat Wang C (1994) A theory of generalization in learning machines with neural application, Ph.D. thesis. The University of Pennsylvania Wang C (1994) A theory of generalization in learning machines with neural application, Ph.D. thesis. The University of Pennsylvania
54.
Zurück zum Zitat Masters T (1994) Practical neural network recipes in C++. Academic Press, BostonMATH Masters T (1994) Practical neural network recipes in C++. Academic Press, BostonMATH
55.
Zurück zum Zitat Kaastra I, Boyd M (1996) Designing a neural network for forecasting financial and economic time series. Neurocomputing 10:215–236CrossRef Kaastra I, Boyd M (1996) Designing a neural network for forecasting financial and economic time series. Neurocomputing 10:215–236CrossRef
56.
Zurück zum Zitat Kanellopoulas I, Wilkinson GG (1997) Strategies and best practice for neural network image classification. Int J Remote Sens 18:711–725CrossRef Kanellopoulas I, Wilkinson GG (1997) Strategies and best practice for neural network image classification. Int J Remote Sens 18:711–725CrossRef
57.
Zurück zum Zitat Rezaei M, Monjezi M, Yazdian Varjani A (2011) Development of a fuzzy model to predict flyrock in surface mining. Saf Sci 49:298–305CrossRef Rezaei M, Monjezi M, Yazdian Varjani A (2011) Development of a fuzzy model to predict flyrock in surface mining. Saf Sci 49:298–305CrossRef
58.
Zurück zum Zitat Tripathy A, Singh TN, Kundu J (2015) Prediction of abrasiveness index of some Indian rocks using soft computing methods. Measurement 68:302–309CrossRef Tripathy A, Singh TN, Kundu J (2015) Prediction of abrasiveness index of some Indian rocks using soft computing methods. Measurement 68:302–309CrossRef
59.
Zurück zum Zitat SPSS Inc (2007) SPSS for Windows (version 16.0). SPSS Inc, Chicago SPSS Inc (2007) SPSS for Windows (version 16.0). SPSS Inc, Chicago
Metadaten
Titel
Airblast prediction through a hybrid genetic algorithm-ANN model
verfasst von
Danial Jahed Armaghani
Mahdi Hasanipanah
Amir Mahdiyar
Muhd Zaimi Abd Majid
Hassan Bakhshandeh Amnieh
Mahmood M. D. Tahir
Publikationsdatum
14.09.2016
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 9/2018
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-016-2598-8

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