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Engineering with Computers

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01-10-2016 | Original Article

A new hybrid ANFIS–PSO model for prediction of peak particle velocity due to bench blasting

Authors: Ebrahim Ghasemi, Hamid Kalhori, Raheb Bagherpour

Published in: Engineering with Computers | Issue 4/2016

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Abstract

In this paper, a novel hybrid approach is proposed for predicting peak particle velocity (PPV) due to bench blasting in open pit mines. The proposed approach is based on the combination of adaptive neuro-fuzzy inference system (ANFIS) and particle swarm optimization (PSO). In this approach, the PSO is used to improve the performance of ANFIS. Furthermore, a model is developed based on support vector regression (SVR) approach. The models are trained and tested based on actual data compiled from 120 blast rounds in Sarcheshmeh copper mine. To determine the accuracy and efficiency of ANFIS–PSO and SVR models, a statistical model (USBM equation) is applied. According to the obtained results, both techniques can be used to predict the PPV, but the comparison of models shows that the ANFIS–PSO model provides better results. Root mean square error (RMSE), variance account for (VAF), and coefficient of determination (R ²) indices were obtained as 1.83, 93.37 and 0.957 for ANFIS–PSO model, respectively.

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Jimeno CL, Jimeno EL, Carcedo FJA (1995) Drilling and blasting of rocks. Balkema, Rotterdam

Bhandari S (1997) Engineering rock blasting operations. Balkema, Rotterdam

Pal Roy P (2005) Rock blasting effects and operations. Balkema, Rotterdam

Khandelwal M, Singh TN (2007) Evaluation of blast-induced ground vibration predictors. Soil Dynam Earthquake Eng 27:116–125CrossRef

Duvall WI, Petkof BB (1959) Spherical propagation of explosion generated strain pulses in rock. US Bur Mines, RI 5483

Dowding CH (1985) Blast vibration monitoring and control. Prentice-Hall, Englewoods Cliffs

Siskind DE, Stagg MS, Kopp JW, Dowding CH (1980) Structure response and damage produced by ground vibration from surface mine blasting. US Bur Mines, RI 8507

Duvall WI, Fogelson DE (1962) Review of criteria for estimating damage to residences from blasting vibration. US Bur Mines, RI 5968

Edwards AT, Northwood TD (1960) Experimental study of effects of blasting on structures. Engineering 210:539–546

10.

Nicholls HR, Johnson CF, Duvall WI (1971) Blasting vibrations and their effects on structures. US Bur Mines, RI 656

11.

German Institute of Standards (1986) Vibration of building-effects on structures. DIN 4150, Vol 3, pp 1–5

12.

Siskind DE (2000) Vibrations from blasting. ISEE Publication, Cleveland

13.

Duvall WI, Petkof BB (1959) Spherical propagation of explosion generated strain pulses in rock. US Bur Mines, RI 5483

14.

Langefors U, Kihlstrom B (1963) The modern technique of rock blasting. Wiley, New York

15.

Davies B, Farmer IW, Attewell PB (1964) Ground vibrations from shallow sub-surface blasts. Engineering 217:553–559

16.

Ambraseys NN, Hendron AJ (1968) Dynamic behavior of rock masses. In: Stagg KG, Zienkiewicz OC (eds) Rock mechanics in engineering practices. Wiley, London, pp 203–227

17.

Indian Standard Institute (1973) Criteria for safety and design of structures subjected to underground blast. ISI Bull No 6922

18.

Ghosh A, Daemen JK (1983) A simple new blast vibration predictor (based on wave propagation laws). In: Proceedings of the 24th US symposium on rock mechanics, Texas, USA, pp 151–161

19.

Gupta RN, Pal Roy P, Singh B (1987) On a blast induced blast vibration predictor for efficient blasting. In: Proceedings of the 22nd international conference on safety in mines research institute, Beijing, China, pp 1015–1021

20.

Pal Roy P (1991) Vibration control in an opencast mine based on improved blast vibration predictors. Min Sci Tech 12(2):157–165CrossRef

21.

Rai R, Singh TN (2004) A new predictor for ground vibration prediction and its comparison with other predictors. Indian J Eng Mater Sci 11(3):178–184

22.

Ghasemi E, Araei M, Hashemolhosseini H (2012) Development of a fuzzy model for predicting ground vibration caused by rock blasting in surface mining. J Vib Contr 19(5):755–770CrossRef

23.

Singh TN (2004) Artificial neural network approach for prediction and control of ground vibrations in mines. Mining Technol 113(4):251–256CrossRef

24.

Singh TN, Singh V (2005) An intelligent approach to predict and control ground vibration in mines. Geotech Geol Eng 23(3):249–262CrossRef

25.

Khandelwal M, Singh TN (2006) Prediction of blast induced vibrations and frequency in opencast mine: a neural network approach. J Sound Vib 289:711–725CrossRef

26.

Khandelwal M, Singh TN (2009) Prediction of blast-induced ground vibration using artificial neural network. Int J Rock Mech Min Sci 46(7):1214–1222CrossRef

27.

Khandelwal M, Kumar DL, Yellishetty M (2011) Application of soft computing to predict blasting-induced ground vibration. Eng Comput 27(2):117–125CrossRef

28.

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(5):845–851MathSciNetCrossRef

29.

Bakhshandeh Amnieh H, Mozdianfard MR, Siamaki A (2010) Predicting of blasting vibrations in Sarcheshmeh copper mine by neural network. Saf Sci 48(3):319–325CrossRef

30.

Monjezi M, Ahmadi M, Sheikhan M, Bahrami A, Salimi AR (2010) Predicting blast-induced ground vibration using various types of neural networks. Soil Dynam Earthquake Eng 30(11):1233–1236CrossRef

31.

Dehghani H, Ataee-pour M (2011) Development of a model to predict peak particle velocity in a blasting operation. Int J Rock Mech Min Sci 48(1):51–58CrossRef

32.

Kamali M, Ataei M (2011) Prediction of blast induced vibrations in the structures of Karoun III power plant and dam. J Vib Contr 17(4):541–548CrossRef

33.

Monjezi M, Ghafurikalajahi M, Bahrami A (2011) Prediction of blast-induced ground vibration using artificial neural networks. Tunn Undergr Space Technol 26:46–50CrossRef

34.

Monjezi M, Hasanipanah M, Khandelwal M (2013) Evaluation and prediction of blast-induced ground vibration at Shur River Dam, Iran, by artificial neural network. Neural Comput Appl 22:1637–1643CrossRef

35.

Saadat M, Khandelwal M, Monjezi M (2014) An ANN-based approach to predict blast-induced ground vibration of Gol-E-Gohar iron ore mine, Iran. J Rock Mech Geotech Eng 6:67–76CrossRef

36.

Vasovic D, Kostic S, Ravilic M, Trajkovic S (2014) Environmental impact of blasting at Drenovac limestone quarry (Serbia). Environ Earth Sci 72:3915–3928CrossRef

37.

Alvarez-Vigil AE, Gonzalez-Nicieza C, Gayarre Lopez F, Alvarez-Fernandez MI (2012) Predicting blasting propagation velocity and vibration frequency using artificial neural network. Int J Rock Mech Min Sci 55:108–116

38.

Gorgulu K, Arpaz E, Demirci A, Kocaslan A, Dilmac MK, Yuksek AG (2013) Investigation of blast-induced ground vibrations in the Tulu boron open pit mine. Bull Eng Geol Environ 72:555–564CrossRef

39.

Gorgulu K, Arpaz E, Uysal O, Duruturk YS, Yuksek AG, Kocaslan A, Dilmac MK (2015) Investigation of the effects of blasting design parameters and rock properties on blast-induced ground vibrations. Arab J Geosci 8:4269–4278CrossRef

40.

Ghoraba S, Monjezi M, Talebi N, Moghadam MR, Jahed Armaghani D (2015) Prediction of ground vibration caused by blasting operations through a neural network approach: a case study of Gol-E-Gohar Iron Mine, Iran. J Zhejiang Univ Sci A. doi:10.1631/jzus.A1400252

41.

Fisne A, Kuzu C, Hudaverdi T (2011) Prediction of environmental impacts of quarry blasting operation using fuzzy logic. Environ Monit Assess 174(1–4):461–470CrossRef

42.

Iphar M, Yavuz M, Ak H (2008) prediction of ground vibrations resulting from the blasting operations in an open-pit mine by adaptive neuro-fuzzy inference system. Environ Geol 56(1):97–107CrossRef

43.

Rao YS, Rao KM (2009) Prediction of ground vibrations and frequency in opencast mine using neuro-fuzzy technique. J Sci Ind Res 68:292–295

44.

Mohammadi SS, Bakhshandeh Amnieh H, Bahadori M (2011) Prediction ground vibration caused by blasting operations in Sarcheshmeh copper mine considering the charge type by adaptive neuro-fuzzy inference system (ANFIS). Arch Min Sci 56(4):701–710

45.

Ataei M, Kamali M (2013) Prediction of blast-induced vibration by adaptive neuro-fuzzy inference system in Karoun 3 power plant and dam. J Vib Contr 19(12):1906–1914CrossRef

46.

Jahed Armaghani D, Momeni E, Khalil Abad SVAN, Khandelwal M (2015) Feasibility of ANFIS model for prediction of ground vibrations resulting from quarry blasting. Environ Earth Sci 74:2845–2860CrossRef

47.

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 8:9647–9665CrossRef

48.

Mohammadnejad M, Gholami R, Ramazanzadeh A, Jalali ME (2012) Prediction of blast-induced vibrations in limestone quarries using Support Vector Machine. J Vib Contr 18(9):1322–1329CrossRef

49.

Khandelwal M (2010) Evaluation and prediction of blast-induced ground vibration using support vector machine. Int J Rock Mech Min Sci 47:509–516CrossRef

50.

Khandelwal M (2011) Blast-induced ground vibration prediction using support vector machine. Eng Comput 27:193–200CrossRef

51.

Verma AK, Singh TN (2013) Comparative study of cognitive systems for ground vibration measurements. Neural Comput Appl 22:341–350CrossRef

52.

Verma AK, Singh TN, Maheshwar S (2014) Comparative study of intelligent prediction models for pressure wave velocity. J Geosci Geomatic 2(3):130–138

53.

Dindarloo SR (2015) Peak particle velocity prediction using support vector machines: a surface blasting case study. J S Afr Inst Min Metall 115:637–643CrossRef

54.

Hasanipanah M, Monjezi M, Shahnazari A, Jahed Armaghani D, Farazmand A (2015) Feasibility of indirect determination of blast induced ground vibration based on support vector machine. Measurement 75:289–297CrossRef

55.

Verma AK, Singh TN (2011) Intelligent systems for ground vibration measurement: a comparative study. Eng Comput 27(3):225–233CrossRef

56.

Hajihassani M, Jahed Armaghani D, Marto A, Mohamad ET (2015) Ground vibration prediction in quarry blasting through an artificial neural network optimized by imperialist competitive algorithm. Bull Eng Geol Environ 74:873–886CrossRef

57.

Jahed Armaghani D, Hajihassani M, Mohamad ET, Marto A, Noorani SA (2014) Blasting-induced flyrock and ground vibration prediction through an expert artificial neural network based on particle swarm optimization. Arab J Geosci 7:5383–5396CrossRef

58.

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 74:2799–2817CrossRef

59.

Dutta S (2010) Obstacle avoidance of mobile robot using PSO based neuro fuzzy technique. Int J Comput Sci Eng 2:30–304

60.

Pousinho HMI, Mendes VMF, Catalao JPS (2011) A hybrid PSO-ANFIS approach for short-term wind power prediction in Portugal. Energ Convers Manag 52:397–402CrossRef

61.

Jiang HM, Kwong CK, Ip WH, Wong TC (2012) Modeling customer satisfaction for new product development using PSO-based ANFIS approach. Appl Soft Comput 12:726–734CrossRef

62.

Chen MY (2013) A hybrid ANFIS model for business failure prediction utilizing particle swarm optimization and subtractive clustering. Inform Sci 220:180–195CrossRef

63.

Djavareshkian MH, Esmaeili A (2014) Heuristic optimization of submerged hydrofoil using ANFIS-PSO. Ocean Eng 92:55–63CrossRef

64.

Basser H, Karami H, Shamshirband S, Akib S, Amirmojahedi M, Ahmad R, Jahangirzadeh A, Javidnia H (2015) Hybrid ANFIS–PSO approach for predicting optimum parameters of a protective spur dike. Appl Soft Comput 30:642–649CrossRef

65.

Jang JSR (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23:665–685CrossRef

66.

Kennedy J, Eberhart RC (1995) Particle swarm optimization, In: Proceedings of IEEE international conference on neural networks, Perth, Australia, pp 1942–1948

67.

Jang JSR, Sun CT, Mizutani E (1997) Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence. Prentice Hall, Upper Saddle River

68.

Sumathi S, Paneerselvam S (2010) Computational intelligence paradigms: theory and applications using Matlab. CRC Press, New York

69.

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

70.

Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20:273–297MATH

71.

Venkoba Rao B, Gopalakrishna SJ (2009) Hardgrove grindability index prediction using support vector regression. Int J Miner Process 91:55–59CrossRef

72.

Azamathulla HM, Ghani AA, Chang CK, Hasan ZA, Zakaria NA (2010) Machine learning approach to predict sediment load—a case study. CLEAN Soil Air Water 38:969–976CrossRef

73.

Suganyadevi MV, Babulal CK (2014) Support vector regression model for the prediction of loadability of a power system. Appl Soft Comput 24:304–315CrossRef

74.

Yao B, Hu P, Zhang M, Jin M (2014) A support vector machine with the tabu search algorithm for freeway incident detection. Int J Appl Math Comput Sci 24:397–404CrossRefMATH

75.

Tan P, Zhang C, Xia J, Fang QY, Chen G (2015) Estimation of higher heating value of coal based on proximate analysis using support vector regression. Fuel Process Tech 138:298–304CrossRef

76.

Vapnik VN (1999) An overview of statistical learning theory. IEEE Trans Neural Network 10(5):988–999CrossRef

77.

Chang CC, Lin CJ (2011) LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol 2(3):21–27CrossRef

Title: A new hybrid ANFIS–PSO model for prediction of peak particle velocity due to bench blasting
Authors: Ebrahim Ghasemi
Hamid Kalhori
Raheb Bagherpour
Publication date: 01-10-2016
Publisher: Springer London
Published in: Engineering with Computers / Issue 4/2016
Print ISSN: 0177-0667
Electronic ISSN: 1435-5663
DOI: https://doi.org/10.1007/s00366-016-0438-1

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