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Engineering with Computers
Erschienen in: Engineering with Computers 1/2021

16.07.2019 | Original Article

Performance evaluation of hybrid FFA-ANFIS and GA-ANFIS models to predict particle size distribution of a muck-pile after blasting

verfasst von: Jian Zhou, Chuanqi Li, Chelang A. Arslan, Mahdi Hasanipanah, Hassan Bakhshandeh Amnieh

Erschienen in: Engineering with Computers | Ausgabe 1/2021

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Abstract

Accurately predicting the particle size distribution of a muck-pile after blasting is always an important subject for mining industry. Adaptive neuro-fuzzy inference system (ANFIS) has emerged as a synergic intelligent system. The main contribution of this paper is to optimize the premise and consequent parameters of ANFIS by firefly algorithm (FFA) and genetic algorithm (GA). To the best of our knowledge, no research has been published that assesses FFA and GA with ANFIS for fragmentation prediction and no research has tested the efficiency of these models to predict the fragmentation in different time scales as of yet. To show the effectiveness of the proposed ANFIS-FFA and ANFIS-GA models, their modelling accuracy has been compared with ANFIS, support vector regression (SVR) and artificial neural network (ANN). Intelligence predictions of fragmentation by ANFIS-FFA, ANFIS-GA, ANFIS, SVR and ANN are compared with observed values of fragmentation available in 88 blasting event of two quarry mines, Iran. According to the results, both ANFIS-FFA and ANFIS-GA prediction models performed satisfactorily; however, the lowest root mean square error (RMSE) and the highest correlation of determination (R2) values were obtained from ANFIS-GA model. The values of R2 and RMSE obtained from ANFIS-GA, ANFIS-FFA, ANFIS, SVR and ANN models were equal to (0.989, 0.974), (0.981, 1.249), (0.956, 1.591), (0.924, 2.016) and (0.948, 2.554), respectively. Consequently, the proposed ANFIS-GA model has the potential to be used for predicting aims on other fields.
Vorheriger Artikel Squeezing flow of nanofluids of Cu–water and kerosene between two parallel plates by Gegenbauer Wavelet Collocation method
Nächster Artikel An adaptive region segmentation combining surrogate model applied to correlate design variables and performance parameters in a transonic axial compressor

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Literatur
1.
Monjezi M, Bahrami A, Varjani AY (2010) Simultaneous prediction of fragmentation and flyrock in blasting operation using artificial neural networks. Int J Rock Mech Min Sci 47(3):476–480
2.
Shi XZ, Zhou J, Wu B, Huang D, Wei W (2012) Support vector machines approach to mean particle size of rock fragmentation due to bench blasting prediction. Trans Nonferrous Met Soc China 22:432–441
3.
Bakhtavar E, Khoshrou H, Badroddin M (2015) Using dimensional regression analysis to predict the mean particle size of fragmentation by blasting at the Sungun copper mine. Arab J Geosci 8:2111–2120
4.
Hasanipanah M, Armaghani DJ, Monjezi M, Shams S (2016) Risk assessment and prediction of rock fragmentation produced by blasting operation: a rock engineering system. Environ Earth Sci 75(9):808
5.
Hasanipanah M, Bakhshandeh Amnieh H, Arab H, Zamzam MS (2018) Feasibility of PSO–ANFIS model to estimate rock fragmentation produced by mine blasting. Neural Comput Appl 30(4):1015–1024
6.
Mishnaevsky JR, Schmauder S (1996) Analysis of rock fragmentation with the use of the theory of fuzzy sets. In: Barla (ed) Proceedings of the Eurock, ISRM international symposium, vol 96. International Society for Rock Mechanics and Rock Engineering, pp 735–740
7.
Roy PP, Dhar BB (1996) Fragmentation analyzing scale—a new tool for breakage assessment. In: Proceedings 5th international symposium on rock fragmentation by blasting-FRAGBLAST 5. Balkema, Rotterdam
8.
Mohammadhassani M, Nezamabadi-Pour H, Suhatril M, Shariati M (2014) An evolutionary fuzzy modelling approach and comparison of different methods for shear strength prediction of high-strength concrete beams without stirrups. Smart Struct Syst Int J 14(5):785–809
9.
Zhou J, Li X, Mitri HS (2015) Comparative performance of six supervised learning methods for the development of models of hard rock pillar stability prediction. Nat Hazards 79(1):291–316
10.
11.
Zhou J, Shi X, Li X (2016) Utilizing gradient boosted machine for the prediction of damage to residential structures owing to blasting vibrations of open pit mining. J Vib Control 22(19):3986–3997
12.
Mansouri I et al (2016) Strength prediction of rotary brace damper using MLR and MARS. Struct Eng Mech 60(3):471–488
13.
Zhou J, Shi X, Du K, Qiu X, Li X, Mitri HS (2017) Feasibility of random-forest approach for prediction of ground settlements induced by the construction of a shield-driven tunnel. Int J Geomech 17(6):04016129
14.
Wang M, Shi X, Zhou J, Qiu X (2018) Multi-planar detection optimization algorithm for the interval charging structure of large-diameter longhole blasting design based on rock fragmentation aspects. Eng Optim 50(12):2177–2191
15.
Toghroli et al (2018) Evaluation of the parameters affecting the Schmidt rebound hammer reading using ANFIS method. Comput Concrete 21:525–530
16.
Zhou J, Li X, Mitri HS (2018) Evaluation method of rockburst: state-of-the-art literature review. Tunn Undergr Space Technol 81:632–659
17.
Zhou J, Li E, Yang S, Wang M, Shi X, Yao S, Mitri HS (2019) Slope stability prediction for circular mode failure using gradient boosting machine approach based on an updated database of case histories. Saf Sci 118:505–518
18.
19.
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 46:1273–1280
20.
Karami A, Afiuni-Zadeh S (2013) Sizing of rock fragmentation modeling due to bench blasting using adaptive neuro fuzzy inference system (ANFIS). Int J Min Sci Technol 23(6):809–813
21.
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(1):27–36
22.
Gao W, Karbasi M, Hasanipanah M, Zhang X, Guo J (2018) Developing GPR model for forecasting the rock fragmentation in surface mines. Eng Comput 34(2):339–345
23.
Asl PF, Monjezi M, Hamidi JK, Armaghani DJ (2018) Optimization of flyrock and rock fragmentation in the Tajareh limestone mine using metaheuristics method of firefly algorithm. Eng Comput 34(2):241–251
24.
Yang Y, Zang O (1997) A hierarchical analysis for rock engineering using artificial neural networks. Rock Mech Rock Eng 30:207–222
25.
Vapnik V, Lerner A (1963) Pattern recognition using generalized portrait method. Autom Remote Control 24:774–780
26.
Vapnik V, Chervonenkis A (1964) A note on one class of perceptrons. Autom Remote Control 25:103–109
27.
Vapnik V, Chervonenkis A (1974) Theory of pattern recognition. Nauka, Moscow, (in Russian); German translation: Theorie der Zeichenerkennung, Akademie Verlag, Berlin, 1979
28.
Vapnik V (1982) Estimation of dependences based on empirical data. Springer Verlag, New York
29.
Vapnik V (1995) The nature of statistical learning theory. Springer Verlag, New York
30.
Vapnik V, Golowich S, Smola A (1997) Support vector method for function approximation, regression estimation, and signal processing. In: Mozer M, Jordan M, Petsche T (eds) Neural information processing systems, vol 9. MIT Press, Cambridge
31.
Basak D, Pal S, Patranabis DC (2007) Support vector regression. Neural Inf Process Lett Rev 11(10):203–224
32.
Zhou J, Li X, Shi X (2012) Long-term prediction model of rockburst in underground openings using heuristic algorithms and support vector machines. Saf Sci 50(4):629–644
33.
34.
Chang CC, Lin CJ (2001) A library for support vector machines, Technical Report, Department of Computer Science and Information Engineering, National Taiwan University
35.
Anandhi V, Chezian RM (2013) Support vector regression to forecast demand and supply Pulpwood. Int J Future Commun 2(3):266
36.
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(7–8):1637–1643
37.
Taheri K, Hasanipanah M, Bagheri Golzar S, Majid MZA (2016) A hybrid artificial bee colony algorithm-artificial neural network for forecasting the blast-produced ground vibration. Eng Comput 33(3):689–700
38.
Hasanipanah M, Noorian-Bidgoli M, Armaghani DJ, Khamesi H (2016) Feasibility of PSO-ANN model for predicting surface settlement caused by tunneling. Eng Comput 32(4):705–715
39.
Hecht-Nielsen R (1987) Kolmogorov’s mapping neural network existence theorem. In: Proceedings of the first IEEE international conference on neural networks. San Diego, CA, pp 11–14
40.
Hornik K, Stinchcombe M, White H (1989) Multilayer feedforward networks are universal approximators. Neural Netw 2:359–366
41.
Toghroli A, Mohammadhassani M, Shariati M, Suhatril M, Ibrahim Z, Sulong NHR (2014) Prediction of shear capacity of channel shear connectors using the ANFIS model. Steel Compos Struct J 17(5):623–639
42.
43.
Safa M et al (2016) Potential of adaptive neuro fuzzy inference system for evaluating the factors affecting steel-concrete composite beam’s shear strength. Steel Compos Struct 21(3):679–688
44.
Hasanipanah M, Armaghani DJ, Khamesi H, Amnieh HB, Ghoraba S (2016) Several non-linear models in estimating air-overpressure resulting from mine blasting. Eng Comput 32(3):441–455
45.
46.
Koçaslan A, Yüksek AG, Görgülü K, Arpaz E (2017) Evaluation of blast-induced ground vibrations in open-pit mines by using adaptive neuro-fuzzy inference systems. Environ Earth Sci 76:57
47.
Sedghi Y et al (2018) Application of ANFIS technique on performance of C and L shaped angle shear connectors. Smart Struct Syst 22(3):335–340MathSciNet
48.
Jalalifar H et al (2011) Application of the adaptive neuro-fuzzy inference system for prediction of a rock engineering classification system. Comput Geotech 38:783–790
49.
Fattahi H, Bayatzadehfard Z (2017) A comparison of performance of several artificial intelligence methods for estimation of required rotational torque to operate horizontal directional drilling. Iran Univ Sci Technol 7:45–70
50.
Yang XS (2008) Firefly algorithm. Nat Inspired Metaheuristic Algorithms 20:79–90
51.
Majumder A, Das A, Das PK (2018) A standard deviation based firefly algorithm for multi-objective optimization of WEDM process during machining of Indian RAFM steel. Neural Comput Appl 29(3):665–677
52.
Kazemivash B, Moghaddam ME (2018) A predictive model-based image watermarking scheme using regression tree and firefly algorithm. Soft Comput 22(12):4083–4098
53.
Zhou J, Li X, Mitri HS (2016) Classification of rockburst in underground projects: comparison of ten supervised learning methods. J Comput Civil Eng 30(5):04016003
54.
Hasanipanah M, Golzar SB, Larki IA, Maryaki MY, Ghahremanians T (2017) Estimation of blast-induced ground vibration through a soft computing framework. Eng Comput 33(4):951–959
55.
Hasanipanah M, Naderi R, Kashir J, Noorani SA, Aaq Qaleh AZ (2017) Prediction of blast produced ground vibration using particle swarm optimization. Eng Comput 33(2):173–179
56.
Hasanipanah M, Shahnazar A, Amnieh HB, Armaghani DJ (2017) Prediction of air-overpressure caused by mine blasting using a new hybrid PSO–SVR model. Eng Comput 33(1):23–31
57.
Hasanipanah M, Shahnazar A, Arab H, Golzar SB, Amiri M (2017) Developing a new hybrid-AI model to predict blast induced backbreak. Eng Comput 33(3):349–359
58.
Hasanipanah M, Faradonbeh RS, Amnieh HB, Armaghani DJ, Monjezi M (2017) Forecasting blast induced ground vibration developing a CART model. Eng Comput 33(2):307–316
59.
Hasanipanah M, Faradonbeh RS, Armaghani DJ, Amnieh HB, Khandelwal M (2017) Development of a precise model for prediction of blast-induced flyrock using regression tree technique. Environ Earth Sci 76(1):27
60.
Hasanipanah M, Armaghani DJ, Amnieh HB, Majid MZA, Tahir MMD (2017) Application of PSO to develop a powerful equation for prediction of flyrock due to blasting. Neural Comput Appl 28(1):1043–1050
61.
Wang M, Shi X, Zhou J (2018) Charge design scheme optimization for ring blasting based on the developed Scaled Heelan model. Int J Rock Mech Min Sci 110:199–209
62.
Rad HN, Hasanipanah M, Rezaei M, Eghlim AL (2018) Developing a least squares support vector machine for estimating the blast-induced flyrock. Eng Comput 34(4):709–717
63.
Hasanipanah M et al (2018) Prediction of an environmental issue of mine blasting: an imperialistic competitive algorithm-based fuzzy system. Int J Environ Sci Technol 15(3):551–560
64.
Qi C, Fourie A, Chen Q, Tang X, Zhang Q, Gao R (2018) Data-driven modelling of the flocculation process on mineral processing tailings treatment. J Clean Prod 196:505–516
65.
Keshtegar B, Hasanipanah M, Bakhshayeshi I, Sarafraz ME (2019) A novel nonlinear modeling for the prediction of blast-induced airblast using a modified conjugate FR method. Measurement 131:35–41
66.
67.
Gao W, Alqahtani AS, Mubarakali A, Mavaluru D, Khalafi S (2019) Developing an innovative soft computing scheme for prediction of air overpressure resulting from mine blasting using GMDH optimized by GA. Eng Comput 35(131):1–8
68.
Qi C, Tang X, Dong X, Chen Q, Fourie A, Liu E (2019) Towards intelligent mining for backfill: a genetic programming-based method for strength forecasting of cemented paste backfill. Miner Eng 133:69–79
Metadaten
Titel
Performance evaluation of hybrid FFA-ANFIS and GA-ANFIS models to predict particle size distribution of a muck-pile after blasting
verfasst von
Jian Zhou
Chuanqi Li
Chelang A. Arslan
Mahdi Hasanipanah
Hassan Bakhshandeh Amnieh
Publikationsdatum
16.07.2019
Verlag
Springer London
Erschienen in
Engineering with Computers / Ausgabe 1/2021
Print ISSN: 0177-0667
Elektronische ISSN: 1435-5663
DOI
https://doi.org/10.1007/s00366-019-00822-0

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