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Engineering with Computers
Erschienen in: Engineering with Computers 2/2021

12.12.2019 | Original Article

A new design of evolutionary hybrid optimization of SVR model in predicting the blast-induced ground vibration

verfasst von: Wusi Chen, Mahdi Hasanipanah, Hima Nikafshan Rad, Danial Jahed Armaghani, M. M. Tahir

Erschienen in: Engineering with Computers | Ausgabe 2/2021

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Abstract

This study aims to identify the suitability of hybridizing the firefly algorithm (FA), genetic algorithm (GA), and particle swarm optimization (PSO) with two well-known data-driven models of support vector regression (SVR) and artificial neural network (ANN) to predict blast-induced ground vibration. Here, these combinations are abbreviated using FA–SVR, PSO–SVR, GA–SVR, FA–ANN, PSO–ANN, and GA–ANN models. In addition, a modified FA (MFA) combined with SVR model is also proposed in this study, namely, MFA–SVR. The feasibility of the proposed models is examined using a case study, located in Johor, Malaysia. Then, to provide an objective assessment of performances of the predictive models, their results were compared based on several well known and popular statistical criteria. According to the results, the MFA–SVR with the coefficient of determination (R2) of 0.984 and root mean square error (RMSE) of 0.614 was more accurate model to predict PPV than the PSO–SVR with R2 = 0.977 and RMSE = 0.725, the FA–SVR with R2 = 0.964 and RMSE = 0.923, the GA–SVR with R2 = 0.957 and RMSE = 1.016, the GA–ANN with R2 = 0.936 and RMSE = 1.252, the FA–ANN with R2 = 0.925 and RMSE = 1.368, and the PSO–ANN with R2 = 0.924 and RMSE = 1.366. Consequently, the MFA–SVR model can be sufficiently employed in estimating the ground vibration, and has the capacity to generalize.
Vorheriger Artikel Research on deformation prediction of tunnel surrounding rock using the model combining firefly algorithm and nonlinear auto-regressive dynamic neural network
Nächster Artikel Legendre multiwavelet functions for numerical solution of multi-term time-space convection–diffusion equations of fractional order

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Literatur
1.
Hagan TN (1973) Rock breakage by explosives. In: Proceedings of national symposium on rock fragmentation, Adelaide, Australia, 26–28 February 1973, pp 1–17
2.
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
3.
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
4.
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
5.
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
6.
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
7.
8.
Ozdemir B, Kumral M (2019) A system-wide approach to minimize the operational cost of bench production in open-cast mining operations. Int J Coal Sci Technol 6(1):84–94
9.
Wiss JF, Linehan PW (1978) Control of vibration and air noise from surface coal mines III. USBM Report 103(3)–79:623
10.
Görgülü K, Arpaz E, Demirci A, Koçaslan A, Dilmaç MK, Yüksek AG (2013) Investigation of blast-induced ground vibrations in the Tülü boron open pit mine. Bull Eng Geol Environ 72(3–4):555–564
11.
Pal RP (2005) Rock blasting. IBH, New Delhi
12.
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
13.
Taheri K, Hasanipanah M, Bagheri Golzar S, Majid MZA (2017) A hybrid artificial bee colony algorithm-artificial neural network for forecasting the blast-produced ground vibration. Eng Comput 33(3):689–700
14.
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
15.
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
16.
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
17.
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
18.
Duvall WI, Petkof B (1959) Spherical propagation of explosion generated strain pulses in rock. Report of Investigation. US Bureau of Mines, Pittsburgh, pp 5483–5521
19.
Davies B, Farmer IW, Attewell PB (1964) Ground vibrations from shallow sub-surface blasts. The Engineer 217:553–559
20.
Ambraseys NR, Hendron AJ (1968) Dynamic behavior of rock masses, rock mechanics in engineering practices. Wiley, London
21.
ISI (1973) Criteria for safety and design of structures subjected to underground blast. ISI Bull 6922
22.
Langefors U, Kihlström B (1978) The modern technique of rock blasting, 3rd edn. Wiley, Stockholm
23.
Ghosh A, Daemen JK (1983) A simple new blast vibration predictor. In: Proceedings of the 24th US symposium on rock mechanics, College Station, TX, USA, pp 151–61
24.
Gupta RN, Roy PP, Bagachi A, Singh B (1987) Dynamic effects in various rock mass and their predictions. J Mines Met Fuels 35(11):455–462
25.
Gupta RN, Roy PP, Singh B (1988) On a blast induced blast vibration predictor for efficient blasting. In: Proceedings of 22nd international conference of safety in mines, Beijing, China, 2–6 Nov 1987, pp 1015–1021
26.
Roy PP (1991) Vibration control in an opencast mine based on improved blast vibration predictors. Min Sci Technol 12:157–165
27.
Arpaz E, Uysal Ö, Tola Y, Görgülü K, Cavus M (2012) Comparison of blast-induced ground vibration predictors in Seyitomer coal mine. In: 12th Rock mechanics symposium, Beijing, China, 18–21 October 2011, pp 1161–1163
28.
ISRM (1992) Suggested method for blast vibration monitoring. Int J Rock Mech Min Sci Geol Abstr 29:143–156
29.
Arpaz E (2000) Monitoring and evaluation of blast induced vibrations in some open-pit mines in Turkey. Dissertation, Cumhuriyet University, Sivas (in Turkish)
30.
Blair DP, Spathis AT (1982) Attenuation of explosion-generated pulse in rock masses. J Geophys Res 87(5):3885–3892
31.
Jimeno CL, Jimeno EL, Carcedo FJA (1995) Drilling and blasting of rocks. A.A Balkema, Rotterdam, p 390
32.
Aldaș GGU (2002) Effect of some rock mass properties on blasting induced ground vibration wave characteristics at Orhaneli surface coal mine. Dissertation, Middle East Technical University, Ankara, Turkey
33.
Nguyen H, Bui XN, Tran QH, Moayedi H (2019) Predicting blast-induced peak particle velocity using BGAMs, ANN and SVM: a case study at the Nui Beo open-pit coal mine in Vietnam. Environ Earth Sci 78:479
34.
35.
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
36.
Asteris PG, Kolovos KG (2019) Self-compacting concrete strength prediction using surrogate models. Neural Comput Appl 31:409–424
37.
38.
Jahed Armaghani D, Hasanipanah M, Amnieh HB, Mohamad ET (2018) Feasibility of ICA in approximating ground vibration resulting from mine blasting. Neural Comput Appl 29(9):457–465
39.
Keshtegar B, Hasanipanah M, Bakhshayeshi I, Sarafraz ME (2019) A novel nonlinear modeling for the prediction of blastinduced airblast using a modified conjugate FR method. Measurement 131:35–41
40.
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
41.
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
42.
Faradonbeh RS, Hasanipanah M, Amnieh HB et al (2018) Development of GP and GEP models to estimate an environmental issue induced by blasting operation. Environ Monit Assess 190:351
43.
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
44.
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
45.
Zhou J, Li X, Mitri HS (2016) Classification of rockburst in underground projects: comparison of ten supervised learning methods. J Comput Civ Eng 30(5):04016003
46.
Zhou J, Li X, Mitri HS (2018) Evaluation method of rockburst: state-of-the-art literature review. Tunn Undergr Space Technol 81:632–659
47.
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
48.
Zhou J, Li E, Wang M, Chen X, Shi X, Jiang L (2019) Feasibility of stochastic gradient boosting approach for evaluating seismic liquefaction potential based on SPT and CPT case histories. J Perform Constr Facil 33(3):04019024
49.
Ghasemi E, Ataei M, Hashemolhosseini H (2013) Development of a fuzzy model for predicting ground vibration caused by rock blasting in surface mining. J Vib Control 19(5):755–770
50.
Radojica L, Kostić S, Pantović R, Vasović N (2014) Prediction of blast-produced ground motion in a copper mine. Int J Rock Mech Min Sci 69:19–25
51.
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(3):873–886
52.
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 7:5383–5396
53.
54.
Shahnazar A, Rad HN, Hasanipanah M, Tahir MM, Armaghani DJ, Ghoroqi M (2017) A new developed approach for the prediction of ground vibration using a hybrid PSO-optimized ANFIS-based model. Environ Earth Sci 76(15):527
55.
56.
57.
58.
59.
ISRM (2007) The complete ISRM suggested methods for rock characterization, testing and monitoring: 1974–2006. In: Ulusay R, Hudson JA (eds) Suggested methods prepared by the commission on testing methods. International Society for Rock Mechanics. ISRM Turkish National Group, Ankara
60.
Swingler K (1996) Applying neural networks: a practical guide. Academic Press, New York
61.
Looney CG (1996) Advances in feed-forward neural networks: demystifying knowledge acquiring black boxes. IEEE Trans Knowl Data Eng 8(2):211–226
62.
Nelson M, Illingworth WT (1990) A practical guide to neural nets. Addison-Wesley, Reading
63.
Vapnik NV (1998) Statistical learning theory. Wiley, New YorkMATH
64.
Yan K, Shi C (2010) Prediction of elastic modulus of normal and high strength concrete by support vector machine. Constr Build Mater 24(8):1479–1485
65.
Safarzadegan Gilan S, Bahrami Jovein H, Ramezanianpour AA (2012) Hybrid support vector regression—particle swarm optimization for prediction of compressive strength and RCPT of concretes containing metakaolin. Constr Build Mater 34:321–329
66.
Hasanipanah M, Monjezi M, Shahnazar A, Armaghani DJ, Farazmand A (2015) Feasibility of indirect determination of blast induced ground vibration based on support vector machine. Measurement 75:289–297
67.
Chen Y, Tan H (2017) Short-term prediction of electric demand in building sector via hybrid support vector regression. Appl Energy 204:1363–1374
68.
Gunn S (1998) Support vector machines for classification and regression. ISIS Technical Report
69.
Vapnik VN, Golowich S, Smola A (1997) Support vector method for function approximation, regression estimation and signal processing. In: Mozer M, Jordan M, Petsche T (eds) Advance in neural information processing system, vol 9. MIT Press, Cambridge, pp 281–287
70.
71.
Tawadrous AS, Katsabanis PD (2007) Prediction of surface crown pillar stability using artificial neural networks. Int J Numer Anal Met 31(7):917–931MATH
72.
Rezaei M, Monjezi M, Moghaddam SG, Farzaneh F (2012) Burden prediction in blasting operation using rock geomechanical properties. Arab J Geosci 5:1031–1037
73.
Kosko B (1994) Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence. Prentice Hall, New DelhiMATH
74.
Singh TN, Kanchan R, Saigal K, Verma AK (2004) Prediction of P-wave velocity and anisotropic properties of rock using artificial neural networks technique. J Sci Ind Res India 63:32–38
75.
76.
77.
Li J et al (2019) Hybrid soft computing approach for determining water quality indicator: euphrates River. Neural Comput Appl 31:827–837
78.
Yang XS (2009) Firefly algorithms for multimodal optimization. In: Stochastic algorithms: foundations and applications SAGA 2009, Lecture Notes in Computer Science (vol. 5792, pp 169–178)
79.
Mohammadi S, Mozafari B, Solimani S, Niknam T (2013) An adaptive modified firefly optimisation algorithm based on hong’s point estimate method to optimal operation management in a microgrid with consideration of uncertainties. Energy 51:339–348
80.
Kennedy J, Eberhart R (1995) Particle swarm optimization. In: IEEE international conference on neural networks, pp 1942–1948
81.
Ren F, Wu X, Zhang K, Niu R (2015) Application of wavelet analysis and a particle swarm-optimized support vector machine to predict the displacement of the Shuping landslide in the Three Gorges, China. Environ Earth Sci 73:4791–4804
82.
83.
Eskandar H, Heydari E, Hasanipanah M, Jalil Masir M, Mahmodi Derakhsh A (2018) Feasibility of particle swarm optimization and multiple regression for the prediction of an environmental issue of mine blasting. Eng Comput 35(1):363–376
84.
Abdi MJ, Salimi H (2010) Farsi handwriting recognition with mixture of RBF experts based on particle swarm optimization. Int J Inf Sci Comput Math 2:129–136
85.
Wei J, Jian-qi Z, Xiang Z (2011) Face recognition method based on support vector machine and particle swarm optimization. Expert Syst Appl 38:4390–4393
86.
Abdi MJ, Giveki D (2013) Automatic detection of erythemato-squamous diseases using PSO–SVM based on association rules. Eng Appl Artif Intell 26:603–608
87.
Holland J (1975) Adaptation in natural and artificial systems. The University of Michigan Press, Ann Arbor
88.
Chipperfield A, Fleming P, Pohlheim H et al (2006) Genetic algorithm toolbox for use with MATLAB user’s guide, version 1.2. University of Sheffield
89.
Simpson AR, Dandy GC, Murphy LJ (1994) Genetic algorithms compared to other techniques for pipe optimization. J Water Res PL-ASCE 120:423–443
90.
91.
92.
Behzadafshar K, Esfandi Sarafraz M, Hasanipanah M, Mojtahedi SFS, Tahir MM (2019) Proposing a new model to approximate the elasticity modulus of granite rock samples based on laboratory tests results. Bull Eng Geol Environ 78(3):1527–1536
93.
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 47:246–253
94.
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
95.
Momeni E, Nazir R, Jahed Armaghani D, Maizir H (2014) Prediction of pile bearing capacity using a hybrid genetic algorithm-based ANN. Measurement 57:122–131
96.
Lazzús JA, Salfate I, Montecinos S (2014) hybrid neural network {particle swarm algorithm to describe chaotic time series. Neural Netw World 6(14):601–617
97.
Saemi M, Ahmadi M, Varjani A (2007) Design of neural networks using genetic algorithm for the permeability estimation of the reservoir. J Pet Sci Eng 59:97–105
98.
Samadianfard S, Ghorbani MA, Mohammadi B (2018) Forecasting soil temperature at multiple-depth with a hybrid artificial neural network model coupled-hybrid firefly optimizer algorithm. Inf Process Agric 5:465–476
99.
100.
101.
Yang XS (2010) Engineering optimization: an introduction with metaheuristic applications. Wiley, Hoboken
102.
Baykasoğlu A, Ozsoydan FB (2014) An improved firefly algorithm for solving dynamic multidimensional knapsack problems. Expert Syst Appl 41(8):3712–3725
103.
104.
Asteris PG, Nozhati S, Nikoo M, Cavaleri L, Nikoo M (2019) Krill herd algorithm-based neural network in structural seismic reliability evaluation. Mech Adv Mater Struct 26(13):1146–1153
105.
Asteris PG, Roussis PC, Douvika MG (2017) Feed-forward neural network prediction of the mechanical properties of sandcrete materials. Sensors 17(6):1344
106.
Asteris PG, Armaghani DJ, Hatzigeorgiou Karayannis CG, Pilakoutas K (2019) Predicting the shear strength of reinforced concrete beams using artificial neural networks. Comput Concr 24(5):469–488
107.
Yang Y, Zang O (1997) A hierarchical analysis for rock engineering using artificial neural networks. Rock Mech Rock Eng 30:207–222
Metadaten
Titel
A new design of evolutionary hybrid optimization of SVR model in predicting the blast-induced ground vibration
verfasst von
Wusi Chen
Mahdi Hasanipanah
Hima Nikafshan Rad
Danial Jahed Armaghani
M. M. Tahir
Publikationsdatum
12.12.2019
Verlag
Springer London
Erschienen in
Engineering with Computers / Ausgabe 2/2021
Print ISSN: 0177-0667
Elektronische ISSN: 1435-5663
DOI
https://doi.org/10.1007/s00366-019-00895-x

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