Abstract
With favorable fragmentation during blasting, environmental issues such as ground vibration and air overpressure (AOp) have remained challenging issues for any mining or civil engineering project. Explosives’ accessories have been developed over the years such as ordinary electric detonators, millisecond delay detonators, and electronic delay detonators. Geomechanical properties of rock mass, explosives charge per delay, and distance between blast and monitoring point plays an important role in these two environmental issues of blasting. Airblast or AOp when gases are vented out during explosion to the atmosphere through various mechanisms such as rupturing or rock, blowing out of stemming material, displacement, and colliding of rock during blasting. Many researchers developed empirical equations for prediction of ground vibration. Similar equations were developed for prediction of AOp based on maximum charge per delay and distance. Empirical equations or statistical methods were not accurate for prediction of these environmental issues. During last decade, various artificial intelligence and machine learning techniques such as artificial neural network, neuro-fuzzy, fuzzy logic, support vector machine, and various hybrid models were developed with acceptable accuracy for prediction of ground vibration and AOp resulting from blasting. The mentioned models were reviewed and discussed in detail with their used input variables and accuracy and the best models among them were highlighted and suggested to be used by researchers and designers.
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References
B.R. Murlidhar, D.J. Armaghani, E.T. Mohamad, Intelligence prediction of some selected environmental issues of blasting: a review. Open Constr. Build. Technol. J. 14(1), 298–308 (2020)
H. Grobler, Using electronic detonators to improve all-round blasting performances. Fragblast 7(1), 1–12 (2003)
M. Khandelwal, T.N. Singh, Prediction of blast induced vibrations and frequency in opencast mine: a neural network approach. J. Sound Vib. 289, 711–772 (2006)
M. Khandelwal, T. Singh, Evaluation of blast-induced ground vibration predictors. Soil Dyn. Earthq. Eng. 27(2), 116–125 (2007)
W.I. Duvall, B. Petkof, Spherical Propagation of Explosion-Generated Strain Pulses in Rock, no. 5481–5485 (US Department of the Interior, Bureau of Mines, 1959)
P.K. Singh, A. Sinha, Rock Fragmentation by Blasting: Fragblast 10 (CRC Press, 2013)
E.T. Mohamad, D. Li, B.R. Murlidhar, D.J. Armaghani, K.A. Kassim, I. Komoo, The effects of ABC, ICA, and PSO optimization techniques on prediction of rip** production. Eng. Comput. (2019). https://doi.org/10.1007/s00366-019-00770-9
Z. He, D.J. Armaghani, M. Masoumnezhad, M. Khandelwal, J. Zhou, B.R. Murlidhar, A combination of expert-based system and advanced decision-tree algorithms to predict air-overpressure resulting from quarry blasting. Nat. Resour. Res. 30(2), 1889–1903 (2021)
B.R. Murlidhar, D.J. Armaghani, E.T. Mohamad, S. Changthan, Rock fragmentation prediction through a new hybrid model based on imperial competitive algorithm and neural network. Smart Constr. Res. 2(3), 1–12 (2018)
H. Naderpour, D. Rezazadeh Eidgahee, P. Fakharian, A.H. Rafiean, S.M. Kalantari, A new proposed approach for moment capacity estimation of ferrocement members using Group Method of Data Handling. Eng. Sci. Technol. an. Int. J. 23, 382–391 (2019). https://doi.org/10.1016/j.jestch.2019.05.013
E. Ford, K. Maneparambil, N. Neithalath, Machine learning on microstructural chemical maps to classify component phases in cement pastes. J. Soft Comput. Civ. Eng. 5(4), 1–20 (2021). https://doi.org/10.22115/SCCE.2021.302400.1357
D. Jahed Armaghani, A. Azizi, A comparative study of artificial intelligence techniques to estimate TBM performance in various weathering zones, in Applications of Artificial Intelligence in Tunnelling and Underground Space Technology. SpringerBriefs in Applied Sciences and Technology (Springer, Singapore, 2021), pp. 55–70. https://doi.org/10.1007/978-981-16-1034-9_4
A. Saber, Effects of window-to-wall ratio on energy consumption: application of numerical and ann approaches. J. Soft Comput. Civ. Eng. 5(4), 41–56 (2021). https://doi.org/10.22115/SCCE.2021.281977.1299
A. Khademi, K. Behfarnia, T. Kalman Šipoš, I. Miličević, I. The use of machine learning models in estimating the compressive strength of recycled brick aggregate concrete. Comput. Eng. Phys. Model. 4(4), 1–25 (2021). https://doi.org/10.22115/cepm.2021.297016.1181
Y.-B. Yang, H.-H. Hung, A parametric study of wave barriers for reduction of train-induced vibrations. Int. J. Numer. Methods Eng. 40(20), 3729–3747 (1997)
D.J. Armaghani, M. Hajihassani, E.T. Mohamad, A. Marto, S.A. Noorani, Blasting-induced flyrock and ground vibration prediction through an expert artificial neural network based on particle swarm optimization. Arab. J. Geosci. 7(12), 5383–5396 (2014)
R. Kumar, D. Choudhury, K. Bhargava, Determination of blast-induced ground vibration equations for rocks using mechanical and geological properties. J. Rock Mech. Geotech. Eng. 8(3) (2016)
D. Singh, V. Sastry, Influence of structural discontinuity on rock fragmentation by blasting, in Proceedings of the 6th International Symposium on Intense Dynamic Loading and Its Effects 3–7 June (1986)
C.J. Konya, E.J. Walter, Surface Blast Design (Prentice Hall, Englewood Cliffs, 1990)
P.-A. Persson, R. Holmberg, J. Lee, Rock Blasting and Explosives Engineering (CRC Press, Boca Raton, 1993)
C. Kuzu, A. Fisne, S.G. Ercelebi, Operational and geological parameters in the assessing blast induced airblast-overpressure in quarries. Appl. Acoust. 70(3), 404–411 (2009)
A. Richards, Elliptical airblast overpressure model. Min. Technol. 119(4), 205–211 (2010)
A.K. Rahul, N. Shivhare, S. Kumar, S.B. Dwivedi, P.K.S. Dikshit, Modelling of daily suspended sediment concentration using FFBPNN and SVM algorithms. J. Soft Comput. Civ. Eng. 5(2), 120–134 (2021). https://doi.org/10.22115/SCCE.2021.283137.1305
M.G. Meharie, N. Shaik, Predicting highway construction costs: comparison of the performance of random forest, neural network and support vector machine models. J. Soft Comput. Civ. Eng. 4(2),103–112 (2020). https://doi.org/10.22115/SCCE.2020.226883.1205
L.A. Zadeh, Fuzzy sets. Inf. Control 8(3), 338–353 (1965)
H. Nguyen, X.-N. Bui, H.-B. Bui, D.T. Cuong, Develo** an XGBoost model to predict blast-induced peak particle velocity in an open-pit mine: a case study. Acta Geophys. 67(2), 477–490 (2019)
X.-N. Bui, P. Jaroonpattanapong, H. Nguyen, Q.-H. Tran, N.Q. Long, A novel hybrid model for predicting blast-induced ground vibration based on k-nearest neighbors and particle Swarm optimization. Sci. Rep. 9(1), 1–14 (2019)
J. Huang, M. Koopialipoor, D.J. Armaghani, A combination of fuzzy Delphi method and hybrid ANN-based systems to forecast ground vibration resulting from blasting. Sci. Rep. 10(1), 1–21 (2020)
H. Nguyen, Y. Choi, X.-N. Bui, T. Nguyen-Thoi, Predicting blast-induced ground vibration in open-pit mines using vibration sensors and support vector regression-based optimization algorithms. Sensors 20(1), 132 (2020)
H. Yang, M. Hasanipanah, M. Tahir, D.T. Bui, Intelligent prediction of blasting-induced ground vibration using ANFIS optimized by GA and PSO. Nat. Resour. Res. 29(2), 739–750 (2020)
H. Nguyen, Support vector regression approach with different kernel functions for predicting blast-induced ground vibration: a case study in an open-pit coal mine of Vietnam. SN Appl. Sci. 1(4), 283 (2019)
M.T. Mohamed, Performance of fuzzy logic and artificial neural network in prediction of ground and air vibrations. Int. J. Rock Mech. Min. Sci. 48(5), 845 (2011)
H. Zhang, J. Zhou, D.J. Armaghani, M.M. Tahir, B.T. Pham, V. Van Huynh, A combination of feature selection and random forest techniques to solve a problem related to blast-induced ground vibration. Appl. Sci. 10(3), 869 (2020)
X.-N. Bui et al., Prediction of blast-induced ground vibration intensity in open-pit mines using unmanned aerial vehicle and a novel intelligence system. Nat. Resour. Res. 29(2), 771–790 (2020)
Z. Ding, H. Nguyen, X.-N. Bui, J. Zhou, H. Moayedi, Computational intelligence model for estimating intensity of blast-induced ground vibration in a mine based on imperialist competitive and extreme gradient boosting algorithms. Nat. Resour. Res. 29(2), 751–769 (2020)
M. Khandelwal, T. Singh, Prediction of blast-induced ground vibration using artificial neural network. Int. J. Rock Mech. Min. Sci. 46(7), 1214–1222 (2009)
M. Monjezi, A. Bahrami, A. Varjani, Simultaneous prediction of fragmentation and flyrock in blasting operation using artificial neural networks. Int. J. Rock Mech. Min. Sci. 47(3), 476–480 (2010)
A. Fişne, C. Kuzu, T. Hüdaverdi, Prediction of environmental impacts of quarry blasting operation using fuzzy logic. Environ. Monit. Assess. 174(1–4), 461–470 (2011)
M. Mohammadnejad, R. Gholami, A. Ramezanzadeh, M. Jalali, Prediction of blast-induced vibrations in limestone quarries using Support Vector Machine. J. Vib. Control 18(9), 1322–1329 (2011)
M. Monjezi, A. Mehrdanesh, A. Malek, M. Khandelwal, Evaluation of effect of blast design parameters on flyrock using artificial neural networks. Neural Comput. Appl. 23(2), 349–356 (2013)
D. Jahed Armaghani, M. Hajihassani, A. Marto, R. Shirani Faradonbeh, E.T. Mohamad, Prediction of blast-induced air overpressure: a hybrid AI-based predictive model. Environ. Monit. Assess. 187(11) (2015)
S. Ghoraba, M. Monjezi, N. Talebi, M.R. Moghadam, D. Jahed Armaghani, 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 (2015)
M. Amiri, H.B. Amnieh, M. Hasanipanah, L.M. Khanli, A new combination of artificial neural network and K-nearest neighbors models to predict blast-induced ground vibration and air-overpressure. Eng. Comput. 32(4), 631–644 (2016)
R. Bhatawdekar, P. Sharma, L. Sarma, A. Singh, T. Singh, T. Edy, Prediction of ground vibration and frequency due to blasting, using artificial neural network at a limestone quarry, in Proceedings of National Cement Building Material, Seminar (2017)
H. Nguyen, X.-N. Bui, Q.-H. Tran, T.-Q. Le, N.-H. Do, Evaluating and predicting blast-induced ground vibration in open-cast mine using ANN: a case study in Vietnam. SN Appl. Sci. 1(1), 125 (2019)
H. Yang, H.N. Rad, M. Hasanipanah, H.B. Amnieh, A. Nekouie, Prediction of vibration velocity generated in mine blasting using support vector regression improved by optimization algorithms. Nat. Resour. Res. 29(2), 807–830 (2020)
Q. Fang, H. Nguyen, X.-N. Bui, T. Nguyen-Thoi, Prediction of blast-induced ground vibration in open-pit mines using a new technique based on imperialist competitive algorithm and M5Rules. Nat. Resour. Res. 29(2), 791–806 (2020)
M. Monjezi, M. Ghafurikalajahi, A. Bahrami, Prediction of blast-induced ground vibration using artificial neural networks. Tunn. Undergr. Sp. Technol. 26(1), 46–50 (2011)
E. Ghasemi, M. Ataei, H. Hashemolhosseini, Development of a fuzzy model for predicting ground vibration caused by rock blasting in surface mining. J. Vib. Control 19(5), 755–770 (2013)
M. Hajihassani, D. Jahed Armaghani, A. Marto, E. Tonnizam Mohamad, Ground vibration prediction in quarry blasting through an artificial neural network optimized by imperialist competitive algorithm. Bull. Eng. Geol. Environ. 74, 873–886 (2014)
M. Hajihassani, D. Jahed Armaghani, M. Monjezi, E.T. Mohamad, A. Marto, Blast-induced air and ground vibration prediction: a particle swarm optimization-based artificial neural network approach. Environ. Earth Sci. 74(4), 2799–2817 (2015)
S. Mojtahedi, I. Ebtehaj, M. Hasanipanah, H. Bonakdari, H. Amnieh, Proposing a novel hybrid intelligent model for the simulation of particle size distribution resulting from blasting. Eng. Comput. 35(1), 47–56 (2018)
Y. Azimi, S. H. Khoshrou, M. Osanloo, Prediction of blast induced ground vibration (BIGV) of quarry mining using hybrid genetic algorithm optimized artificial neural network. Measurement 147, 106874 (2019)
W. Jiang, C.A. Arslan, M.S. Tehrani, M. Khorami, M. Hasanipanah, Simulating the peak particle velocity in rock blasting projects using a neuro-fuzzy inference system. Eng. Comput. 35(4), 1203–1211 (2019)
H. Nguyen, X.-N. Bui, H. Moayedi, A comparison of advanced computational models and experimental techniques in predicting blast-induced ground vibration in open-pit coal mine. Acta Geophys. 67(4), 1025–1037 (2019)
X. Zhang et al., Novel soft computing model for predicting blast-induced ground vibration in open-pit mines based on particle swarm optimization and XGBoost. Nat. Resour. Res. 29(2), 711–721 (2020)
J. Zhou, P.G. Asteris, D.J. Armaghani, B.T. Pham, Prediction of ground vibration induced by blasting operations through the use of the Bayesian network and random forest models. Soil Dyn. Earthq. Eng. 139(Aug), 106390 (2020)
W. Chen, M. Hasanipanah, H. Nikafshan Rad, D. Jahed Armaghani, M. Tahir, A new design of evolutionary hybrid optimization of SVR model in predicting the blast-induced ground vibration. Eng. Comput. 37(2), 1455–1471 (2021)
M. Khandelwal, T. Singh, Prediction of blast induced air overpressure in opencast mine. Noise Vib. Worldw. 36(2), 7–16 (2005)
D.J. Armaghani et al., Neuro-fuzzy technique to predict air-overpressure induced by blasting. Arab. J. Geosci. 8(12), 10937–10950 (2015)
H. Nguyen, X.-N. Bui, Soft computing models for predicting blast-induced air over-pressure: A novel artificial intelligence approach, Appl. Soft Comput., 106292 (2020)
X.-N. Bui, H. Nguyen, H.-A. Le, H.-B. Bui, N.-H. Do, Prediction of blast-induced air over-pressure in open-pit mine: assessment of different artificial intelligence techniques. Nat. Resour. Res. 29(2), 571–591 (2020)
H. Nguyen, X.-N. Bui, Predicting blast-induced air overpressure: a robust artificial intelligence system based on artificial neural networks and random forest. Nat. Resour. Res. 28(3), 893–907 (2019)
X. Zhou, D.J. Armaghani, J. Ye, M. Khari, M.R. Motahari, Hybridization of parametric and non-parametric techniques to predict air over-pressure induced by quarry blasting. Nat. Resour. Res. 30(1), 209–224 (2021)
H. Harandizadeh, D.J. Armaghani, Prediction of air-overpressure induced by blasting using an ANFIS-PNN model optimized by GA. Appl. Soft Comput., 106904 (2020)
D. Armaghani, M. Hasanipanah, E. Mohamad, A combination of the ICA-ANN model to predict air-overpressure resulting from blasting. Eng. Comput. 32, 155–171 (2016)
E. Tonnizam Mohamad, D. Jahed Armaghani, M. Hasanipanah, B.R. Murlidhar, M.N.A. Alel, Estimation of air-overpressure produced by blasting operation through a neuro-genetic technique. Environ. Earth Sci. 75(2), 1–15 (2016)
J. Ye, J. Dalle, R. Nezami, M. Hasanipanah, D.J. Armaghani, Stochastic fractal search-tuned ANFIS model to predict blast-induced air overpressure. Eng. Comput. (2020). https://doi.org/10.1007/s00366-020-01085-w
M. Khandelwal, P. Kankar, Prediction of blast-induced air overpressure using support vector machine. Arab. J. Geosci. 4, 427–433 (2011)
E. Tonnizam Mohamad, M. Hajihassani, D. Jahed Armaghani, A. Marto, Simulation of blasting-induced air overpressure by means of artificial neural networks. Int. Rev. Model. Simulations 5(6) (2012)
W. Gao, M. Karbasi, A.M. Derakhsh, A. Jalili, Development of a novel soft-computing framework for the simulation aims: a case study. Eng. Comput. 35(1), 315–322 (2018)
X.-N. Bui et al., A lasso and elastic-net regularized generalized linear model for predicting blast-induced air over-pressure in open-pit mines. InĹĽynieria Mineralna 21 (2019)
W. Gao, A.S. Alqahtani, A. Mubarakali, D. Mavaluru, S. khalafi, Develo** an innovative soft computing scheme for prediction of air overpressure resulting from mine blasting using GMDH optimized by GA. Eng. Comput. 36(2), 647–654 (2020)
B. Murlidhar, B. Bejarbaneh, D. Armaghani, A. Mohammed, E. Mohamad, Application of tree-based predictive models to forecast air overpressure induced by mine blasting. Nat. Resour. Res. (2020). https://doi.org/10.1007/s11053-020-09770-9
H. Nguyen, X.-N. Bui, H.-B. Bui, N.-L. Mai, A comparative study of artificial neural networks in predicting blast-induced air-blast overpressure at Deo Nai open-pit coal mine, Vietnam. Neural Comput. Appl. 32(8), 3939–3955 (2020)
H. Nguyen et al., “A comparative study of empirical and ensemble machine learning algorithms in predicting air over-pressure in open-pit coal mine. Acta Geophys. (2020). https://doi.org/10.1007/s11600-019-00396-x
H. Nguyen et al., A comparative study of different artificial intelligence techniques in predicting blast-induced air over-pressure. 1(2) (Techno-Press, 2020)
V.A. Temeng, Y.Y. Ziggah, C.K. Arthur, A novel artificial intelligent model for predicting air overpressure using brain inspired emotional neural network. Int. J. Min. Sci. Technol. (2020). https://doi.org/10.1016/j.ijmst.2020.05.020
J. Zhou, A. Nekouie, C.A. Arslan, B.T. Pham, M. Hasanipanah, Novel approach for forecasting the blast-induced AOp using a hybrid fuzzy system and firefly algorithm. Eng. Comput. 36(2), 703–712 (2020)
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Bhatawdekar, R.M., Armaghani, D.J., Azizi, A. (2021). Blast-Induced Air and Ground Vibrations: A Review of Soft Computing Techniques. In: Environmental Issues of Blasting. SpringerBriefs in Applied Sciences and Technology. Springer, Singapore. https://doi.org/10.1007/978-981-16-8237-7_4
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