Skip to main content

Advertisement

SpringerLink
Log in

Application of Tree-Based Predictive Models to Forecast Air Overpressure Induced by Mine Blasting

  • Original Paper
  • Published:
Natural Resources Research Aims and scope Submit manuscript

Abstract

In surface mines and underground excavations, every blasting operation can have some destructive environmental impacts, among which air overpressure (AOp) is of major significance. Therefore, it is essential to minimize the related environmental damage by precisely evaluating the intensity of AOp before any blasting operation. The present study primarily aimed to develop two different tree-based data mining algorithms, namely M5′ decision tree and genetic programming (GP) for accurately predicting blast-induced AOp in granite quarries. In addition, a multiple linear regression technique was adopted to check the accuracy of the GP and M5′ models. To achieve the aims of this research, 125 blasts were explored and their respective AOp values were carefully recorded. In each operation, six influential parameters of AOp, i.e., stemming length, powder factor, blasting index, joint aperture, maximum charge weight per delay and distance of the blast points, were recorded and considered as inputs for modeling. After developing the predictive models of AOp, their performances were examined in terms of coefficient of determination (R2), root-mean-squared error (RMSE) and mean absolute error (MAE). Based on the computed results, the GP (with RMSE of 1.3997, R2 of 0.8621 and MAE of 0.9472) outperformed the other developed models. Then, a sensitivity analysis was employed to identify the most influential parameters in predicting the AOp values. Finally, the generality of the proposed GP model was validated by investigating its predictive results with respect to the two most effective predictor variables. The study findings demonstrated the robustness and applicability of the proposed GP model for predicting blast-induced AOp.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13
Figure 14
Figure 15
Figure 16
Figure 17

Similar content being viewed by others

References

  • Abolfathi, S., Yeganeh-Bakhtiary, A., Hamze-Ziabari, S. M., & Borzooei, S. (2016). Wave runup prediction using M5′ model tree algorithm. Ocean Engineering, 112, 76–81.

    Google Scholar 

  • Armaghani, J. D., Asteris, P. G., Askarian, B., Hasanipanah, M., Tarinejad, R., & Van Huynh, V. (2020a). Examining hybrid and single SVM models with different kernels to predict rock brittleness. Sustainability, 12(6), 2229.

    Google Scholar 

  • Armaghani, D. J., Hajihassani, M., Mohamad, E. T., Marto, A., & Noorani, S. A. (2014). Blasting-induced flyrock and ground vibration prediction through an expert artificial neural network based on particle swarm optimization. Arabian Journal of Geosciences, 7(12), 5383–5396.

    Google Scholar 

  • Armaghani, J. D., Hasanipanah, M., Mahdiyar, A., Abd Majid, M. Z., Bakhshandeh Amnieh, H., & Tahir, M. M. D. (2018). Airblast prediction through a hybrid genetic algorithm-ANN model. Neural Computing and Applications, 29(9), 619–629.

    Google Scholar 

  • Armaghani, D. J., Hasanipanah, M., & Mohamad, E. T. (2016a). A combination of the ICA-ANN model to predict air-overpressure resulting from blasting. Engineering with Computers, 32(1), 155–171.

    Google Scholar 

  • Armaghani, D. J., Koopialipoor, M., Bahri, M., Hasanipanah, M., & Tahir, M. M. (2020b). A SVR-GWO technique to minimize flyrock distance resulting from blasting. Bulletin of Engineering Geology and the Environment. https://doi.org/10.1007/s10064-020-01834-7.

    Article  Google Scholar 

  • Armaghani, D. J., Koopialipoor, M., Marto, A., & Yagiz, S. (2019). Application of several optimization techniques for estimating TBM advance rate in granitic rocks. Journal of Rock Mechanics and Geotechnical Engineering. https://doi.org/10.1016/j.jrmge.2019.01.002.

    Article  Google Scholar 

  • Armaghani, D. J., Mahdiyar, A., Hasanipanah, M., Faradonbeh, R. S., Khandelwal, M., & Amnieh, H. B. (2016b). Risk assessment and prediction of flyrock distance by combined multiple regression analysis and Monte Carlo simulation of quarry blasting. Rock Mechanics and Rock Engineering, 49(9), 1–11.

    Google Scholar 

  • Armaghani, D. J., Mohamad, E. T., Narayanasamy, M. S., Narita, N., & Yagiz, S. (2017). Development of hybrid intelligent models for predicting TBM penetration rate in hard rock condition. Tunnelling and Underground Space Technology, 63, 29–43.

    Google Scholar 

  • Asadi, M., Eftekhari, M., & Bagheripour, M. H. (2011). Evaluating the strength of intact rocks through genetic programming. Applied Soft Computing, 11(2), 1932–1937.

    Google Scholar 

  • Bahrami, A., Monjezi, M., Goshtasbi, K., et al. (2011). Prediction of rock fragmentation due to blasting using artificial neural network. Engineering with Computers, 27, 177–181.

    Google Scholar 

  • Bakhtavar, E., Abdollahisharif, J., & Ahmadi, M. (2017). Reduction of the undesirable bench-blasting consequences with emphasis on ground vibration using a developed multi-objective stochastic programming. International Journal of Mining, Reclamation and Environment, 31(5), 333–345.

    Google Scholar 

  • Baykasoğlu, A., Güllü, H., Çanakçı, H., & Özbakır, L. (2008). Prediction of compressive and tensile strength of limestone via genetic programming. Expert Systems with Applications, 35(1), 111–123.

    Google Scholar 

  • Beiki, M., Bashari, A., & Majdi, A. (2010). Genetic programming approach for estimating the deformation modulus of rock mass using sensitivity analysis by neural network. International Journal of Rock Mechanics and Mining Sciences, 47(7), 1091–1103.

    Google Scholar 

  • Bejarbaneh, B. Y. (2012). Shear strength parameters of shale based on triaxial compression test. Skudai: Universiti Teknologi Malaysia.

    Google Scholar 

  • Bejarbaneh, B. Y., Bejarbaneh, E. Y., Fahimifar, A., Armaghani, D. J., & Majid, M. Z. A. (2018). Intelligent modelling of sandstone deformation behaviour using fuzzy logic and neural network systems. Bulletin of Engineering Geology and the Environment, 77(1), 345–361.

    Google Scholar 

  • Bhandari, S. (1997). Engineering rock blasting operations. A. A. Balkema, 388, 388.

    Google Scholar 

  • Breiman, L., Friedman, J., Olshen, R., & Stone, C. (1984). Classification and regression trees. Wadsworth International Group, 37(15), 237–251.

    Google Scholar 

  • Cramer, N. L. (1985). A representation for the adaptive generation of simple sequential programs. In Proceedings of the first international conference on genetic algorithms (pp. 183–187).

  • Dalton, J. (2007) Genetic algorithms. Newcastle Engineering Design Centre, Merz Court, Newcastle University. http://www.edc.ncl.ac.uk/highlight/rhjanuary2007g02.php.

  • Dowding, C. H. (1992). Suggested method for blast vibration monitoring. International Journal of Rock Mechanics and Mining Sciences & Geomechanics Abstracts, 29(2), 145–156.

    Google Scholar 

  • Fang, Q., Bejarbaneh, B. Y., Vatandoust, M., Armaghani, D. J., Murlidhar, B. R., & Mohamad, E. T. (2019a). Strength evaluation of granite block samples with different predictive models. Engineering with Computers. https://doi.org/10.1007/s00366-019-00872-4.

    Article  Google Scholar 

  • Fang, Q., Nguyen, H., Bui, X.-N., & Nguyen-Thoi, T. (2019b). Prediction of blast-induced ground vibration in open-pit mines using a new technique based on imperialist competitive algorithm and M5Rules. Natural Resources Research. https://doi.org/10.1007/s11053-019-09577-3.

    Article  Google Scholar 

  • Faradonbeh, R. S., Armaghani, D. J., & Monjezi, M. (2016a). Development of a new model for predicting flyrock distance in quarry blasting: A genetic programming technique. Bulletin of Engineering Geology and the Environment, 75(3), 993–1006.

    Google Scholar 

  • Faradonbeh, R. S., Armaghani, D. J., Monjezi, M., & Mohamad, E. T. (2016b). Genetic programming and gene expression programming for flyrock assessment due to mine blasting. International Journal of Rock Mechanics and Mining Sciences, 88, 254–264.

    Google Scholar 

  • Faradonbeh, R. S., & Taheri, A. (2019). Long-term prediction of rockburst hazard in deep underground openings using three robust data mining techniques. Engineering with Computers, 35(2), 659–675.

    Google Scholar 

  • Gen, M., & Cheng, R. (2000). Genetic algorithms and engineering optimization. Hoboken: Wiley.

    Google Scholar 

  • Gordan, B., Armaghani, D. J., Adnan, A. B., & Rashid, A. S. A. (2016). A new model for determining slope stability based on seismic motion performance. Soil Mechanics and Foundation Engineering, 53(5), 344–351.

    Google Scholar 

  • Hajihassani, M., Armaghani, D. J., & Kalatehjari, R. (2018). Applications of particle swarm optimization in geotechnical engineering: A comprehensive review. Geotechnical and Geological Engineering, 36(2), 705–722.

    Google Scholar 

  • Hajihassani, M., Armaghani, D. J., Monjezi, M., Mohamad, E. T., & Marto, A. (2015). Blast-induced air and ground vibration prediction: A particle swarm optimization-based artificial neural network approach. Environmental Earth Sciences, 74(4), 2799–2817.

    Google Scholar 

  • Hajihassani, M., Armaghani, D. J., Sohaei, H., Tonnizam Mohamad, E., & Marto, A. (2014). Prediction of airblast-overpressure induced by blasting using a hybrid artificial neural network and particle swarm optimization. Applied Acoustics, 80, 57–67.

    Google Scholar 

  • Harandizadeh, H., Armaghani, D. J., & Mohamad, E. T. (2020). Development of fuzzy-GMDH model optimized by GSA to predict rock tensile strength based on experimental datasets. Neural Computing and Applications. https://doi.org/10.1007/s00521-020-04803-z.

    Article  Google Scholar 

  • Hasanipanah, M., Armaghani, D. J., Khamesi, H., Bakhshandeh Amnieh, H., & Ghoraba, S. (2016). Several non-linear models in estimating air-overpressure resulting from mine blasting. Engineering with Computers. https://doi.org/10.1007/s00366-015-0425-y.

    Article  Google Scholar 

  • Hasanipanah, M., & Bakhshandeh Amnieh, H. (2020). A fuzzy rule-based approach to address uncertainty in risk assessment and prediction of blast-induced flyrock in a quarry. Natural Resources Research, 29, 669–689.

    Google Scholar 

  • Hasanipanah, M., Monjezi, M., Shahnazar, A., Armaghani, D. J., & Farazmand, A. (2015). Feasibility of indirect determination of blast induced ground vibration based on support vector machine. Measurement, 75, 289–297.

    Google Scholar 

  • Hasanipanah, M., Shahnazar, A., Bakhshandeh Amnieh, H., & Armaghani, D. J. (2017). Prediction of air-overpressure caused by mine blasting using a new hybrid PSO–SVR model. Engineering with Computers, 33(1), 23–31.

    Google Scholar 

  • Hemphill, G. B. (1981). Blasting operations. New York: McGraw-Hill.

    Google Scholar 

  • Hrnjica, B., & Danandeh Mehr, A. (2018). Optimized genetic programming applications: Emerging research and opportunities. Emerging Research and Opportunities. https://doi.org/10.4018/978-1-5225-6005-0.

    Article  Google Scholar 

  • Huang, L., Asteris, P. G., Koopialipoor, M., Armaghani, D. J., & Tahir, M. M. (2019). Invasive weed optimization technique-based ANN to the prediction of rock tensile strength. Applied Sciences, 9(24), 5372.

    Google Scholar 

  • Jekabsons, G. (2016). M5PrimeLab: M5' regression tree, model tree, and tree ensemble toolbox for Matlab/Octave. Available at http://www.cs.rtu.lv/jekabsons/.

  • Kamari, A., Arabloo, M., Shokrollahi, A., Gharagheizi, F., & Mohammadi, A. H. (2015). Rapid method to estimate the minimum miscibility pressure (MMP) in live reservoir oil systems during CO2 flooding. Fuel, 153, 310–319.

    Google Scholar 

  • Karakus, M. (2011). Function identification for the intrinsic strength and elastic properties of granitic rocks via genetic programming (GP). Computers & Geosciences, 37(9), 1318–1323.

    Google Scholar 

  • Khandelwal, M., Faradonbeh, R. S., Monjezi, M., Armaghani, D. J., Majid, M. Z. B. A., & Yagiz, S. (2017). Function development for appraising brittleness of intact rocks using genetic programming and non-linear multiple regression models. Engineering with Computers, 33(1), 13–21.

    Google Scholar 

  • Khandelwal, M., & Kankar, P. K. (2011). Prediction of blast-induced air overpressure using support vector machine. Arabian Journal of Geosciences, 4(3–4), 427–433.

    Google Scholar 

  • Khandelwal, M., & Singh, T. N. (2005). Prediction of blast induced air overpressure in opencast mine. Noise & Vibration Worldwide, 36(2), 7–16.

    Google Scholar 

  • Khandelwal, M., & Singh, T. N. (2009). Prediction of blast-induced ground vibration using artificial neural network. International Journal of Rock Mechanics and Mining Sciences, 46(7), 1214–1222.

    Google Scholar 

  • Konya, C. J., & Walter, E. J. (1990). Surface blast design. Upper Saddle River: Prentice-Hall.

    Google Scholar 

  • Koopialipoor, M., Armaghani, D. J., Hedayat, A., Marto, A., & Gordan, B. (2019a). Applying various hybrid intelligent systems to evaluate and predict slope stability under static and dynamic conditions. Soft Computing, 23(14), 5913–5929.

    Google Scholar 

  • Koopialipoor, M., Nikouei, S. S., Marto, A., Fahimifar, A., Armaghani, D. J., & Mohamad, E. T. (2018). Predicting tunnel boring machine performance through a new model based on the group method of data handling. Bulletin of Engineering Geology and the Environment, 78(5), 3799–3813.

    Google Scholar 

  • Koopialipoor, M., Noorbakhsh, A., Noroozi Ghaleini, E., Armaghani, D. J., & Yagiz, S. (2019b). A new approach for estimation of rock brittleness based on non-destructive tests. Nondestructive Testing and Evaluation. https://doi.org/10.1080/10589759.2019.1623214.

    Article  Google Scholar 

  • Koopialipoor, M., Tootoonchi, H., Armaghani, D. J., Tonnizam Mohamad, E., & Hedayat, A. (2019c). Application of deep neural networks in predicting the penetration rate of tunnel boring machines. Bulletin of Engineering Geology and the Environment. https://doi.org/10.1007/s10064-019-01538-7.

    Article  Google Scholar 

  • Koza, J. R. (1992). Genetic programming II, automatic discovery of reusable subprograms. Cambridge, MA: MIT Press.

    Google Scholar 

  • Küçüksille, E. U., Selbaş, R., & Şencan, A. (2011). Prediction of thermodynamic properties of refrigerants using data mining. Energy Conversion and Management, 52(2), 836–848.

    Google Scholar 

  • Kuzu, C., Fisne, A., & Ercelebi, S. G. (2009). Operational and geological parameters in the assessing blast induced airblast-overpressure in quarries. Applied Acoustics, 70(3), 404–411.

    Google Scholar 

  • Lilly, P. A. (1986). An empirical method of assessing rock mass blastability. In J. R. Davidson (Ed.), Proceedings of large open pit planning conference (pp. 89–92). Parkville, VIC: The Aus IMM.

  • Lin, Y., Zhou, K., & Li, J. (2018). Application of cloud model in rock burst prediction and performance comparison with three machine learning algorithms. IEEE Access, 6, 30958–30968.

    Google Scholar 

  • Loh, W.-Y., & Shih, Y.-S. (1997). Split selection methods for classification trees. Statistica Sinica, 7, 815–840.

    Google Scholar 

  • Luke, S., & Panait, L. (2006). A comparison of bloat control methods for genetic programming. Evolutionary Computation, 14(3), 309–344.

    Google Scholar 

  • Mahdiyar, A., Armaghani, D. J., Koopialipoor, M., Hedayat, A., Abdullah, A., & Yahya, K. (2020). Practical risk assessment of ground vibrations resulting from blasting, using gene expression programming and Monte Carlo simulation techniques. Applied Sciences, 10(2), 472.

    Google Scholar 

  • McKenzie, C. K. (2009). Flyrock range and fragment size prediction. In Proceedings of the 35th annual conference on explosives and blasting technique, Vol. 2. International Society of Explosives Engineers.

  • Michael, J. A., & Gordon, S. L. (1997). Data mining technique for marketing, sales and customer support (p. 445). New York: Wiley.

    Google Scholar 

  • Mohamad, E. T., Armaghani, D. J., Ghoroqi, M., Bejarbaneh, B. Y., Ghahremanians, T., Majid, M. Z. A., et al. (2017). Ripping production prediction in different weathering zones according to field data. Geotechnical and Geological Engineering, 35(5), 2381–2399.

    Google Scholar 

  • Mohamad, E. T., Armaghani, D. J., Momeni, E., Yazdavar, A. H., & Ebrahimi, M. (2018). Rock strength estimation: A PSO-based BP approach. Neural Computing and Applications, 30(5), 1635–1646.

    Google Scholar 

  • Mohamad, E. T., Armaghani, D. J., & Motaghedi, H. (2013). The effect of geological structure and powder factor in flyrock accident, Masai, Johor, Malaysia. Electronic Journal of Geotechnical Engineering, 18, 5561–5572.

    Google Scholar 

  • Mohamad, E. T., Koopialipoor, M., Murlidhar, B. R., Rashiddel, A., Hedayat, A., & Armaghani, D. J. (2019). A new hybrid method for predicting ripping production in different weathering zones through in situ tests. Measurement. https://doi.org/10.1016/j.measurement.2019.07.054.

    Article  Google Scholar 

  • Mohammadnejad, M., Gholami, R., Sereshki, F., & Jamshidi, A. (2013). A new methodology to predict backbreak in blasting operation. International Journal of Rock Mechanics and Mining Sciences, 1997(60), 75–81.

    Google Scholar 

  • Monjezi, M., Bahrami, A., Varjani, A. Y., et al. (2011). Prediction and controlling of flyrock in blasting operation using artificial neural network. Arabian Journal of Geosciences, 4, 421–425.

    Google Scholar 

  • Nelson, M. M., & Illingworth, W. T. (1991). A practical guide to neural nets (Vol. 1). Reading, MA: Addison-Wesley.

    Google Scholar 

  • Nguyen, H., & Bui, X. N. (2019). Predicting blast-induced air overpressure: A robust artificial intelligence system based on artificial neural networks and random forest. Natural Resources Research, 28(3), 893–907.

    Google Scholar 

  • Poli, R., McPhee, N. F. & Vanneschi, L. (2008) Elitism reduces bloat in genetic programming. In Proceedings of the 10th annual conference on Genetic and evolutionary computation (pp. 1343–1344).

  • Poli, R., McPhee, N. F., & Vanneschi, L. (2009). Analysis of the effects of elitismon bloat in linear and tree-based genetic programming. In R. L. Riolo, T. Soule, & B. Worzel (Eds.), Genetic programming theory and practice VI. Genetic and evolutionary computation (pp. 1–20). Boston, MA: Springer. https://doi.org/10.1007/978-0-387-87623-8_7.

    Chapter  Google Scholar 

  • Pu, Y., Apel, D. B., & Lingga, B. (2018). Rockburst prediction in kimberlite using decision tree with incomplete data. Journal of Sustainable Mining, 17(3), 158–165.

    Google Scholar 

  • Quinlan, J. R. (1986). Induction of decision trees. Machine Learning, 1(1), 81–106.

    Google Scholar 

  • Quinlan, J. R. (1992). Learning with continuous classes. In Proceedings 5th Australian joint conference on artificial intelligence (pp. 343–348). Singapore: World Scientific.

  • Quinlan, J. R. (2014). C4. 5: Programs for machine learning. Amsterdam: Elsevier.

    Google Scholar 

  • Rezaei, M., Monjezi, M., & Varjani, A. (2011). Development of a fuzzy model to predict flyrock in surface mining. Safety Science, 49, 298–305.

    Google Scholar 

  • Richards, A. B. (2010). Elliptical airblast overpressure model. Mining Technology, 119(4), 205–211.

    Google Scholar 

  • Rodríguez, R., Lombardía, C., & Torno, S. (2010). Prediction of the air wave due to blasting inside tunnels: Approximation to a ‘phonometric curve’. Tunnelling and Underground Space Technology, 25(4), 483–489.

    Google Scholar 

  • Sari, M., Ghasemi, E., & Ataei, M. (2014). Stochastic modeling approach for the evaluation of backbreak due to blasting operations in open pit mines. Rock Mechanics and Rock Engineering, 47(2), 771–783.

    Google Scholar 

  • Sawmliana, C., Roy, P. P., Singh, R. K., & Singh, T. N. (2007). Blast induced air overpressure and its prediction using artificial neural network. Mining Technology, 116(2), 41–48.

    Google Scholar 

  • Segarra, P., Domingo, J. F., López, L. M., Sanchidrián, J. A., & Ortega, M. F. (2010). Prediction of near field overpressure from quarry blasting. Applied Acoustics, 71(12), 1169–1176.

    Google Scholar 

  • Shao, Z., Armaghani, D. J., Bejarbaneh, B. Y., Mu’azu, M. A., & Mohamad, E. T. (2019). Estimating the friction angle of black shale core specimens with hybrid-ANN approaches. Measurement. https://doi.org/10.1016/j.measurement.2019.06.007.

    Article  Google Scholar 

  • Sharma, L. K., & Singh, T. N. (2018). Regression-based models for the prediction of unconfined compressive strength of artificially structured soil. Engineering with Computers, 34, 175–186. https://doi.org/10.1007/s00366-017-0528-8.

    Article  Google Scholar 

  • Sikora, M., Krzystanek, Z., Bojko, B., & Śpiechowicz, K. (2011). Application of a hybrid method of machine learning for description and on-line estimation of methane hazard in mine workings. Journal of Mining Science, 47(4), 493–505.

    Google Scholar 

  • Siskind, D. E., Stachura, V. J., Stagg, M. S., & Kopp, J. W. (1980). Structure response and damage produced by airblast from surface mining. Princeton: Citeseer.

    Google Scholar 

  • Sousa, L. R., Miranda, T., e Sousa, R. L., & Tinoco, J. (2017). The use of data mining techniques in rockburst risk assessment. Engineering, 3(4), 552–558.

    Google Scholar 

  • Sun, L., Koopialipoor, M., Armaghani, D. J., Tarinejad, R., & Tahir, M. M. (2019). Applying a meta-heuristic algorithm to predict and optimize compressive strength of concrete samples. Engineering with Computers. https://doi.org/10.1007/s00366-019-00875-1.

  • Swingler, K. (1996). Applying neural networks: A practical guide. New York: Academic Press.

    Google Scholar 

  • Tang, D., Gordan, B., Koopialipoor, M., Armaghani, D. J., Tarinejad, R., Thai Pham, B., et al. (2020). Seepage analysis in short embankments using developing a metaheuristic method based on governing equations. Applied Sciences, 10(5), 1761.

    Google Scholar 

  • Tonnizam Mohamad, E., Armaghani, D. J., Hasanipanah, M., Murlidhar, B. R., & Alel, M. N. A. (2016). Estimation of air-overpressure produced by blasting operation through a neuro-genetic technique. Environmental Earth Sciences, 75(2), 1–15.

    Google Scholar 

  • Tonnizam Mohamad, E., Hajihassani, M., Armaghani, D. J., & Marto, A. (2012). Simulation of blasting-induced air overpressure by means of Artificial Neural Networks. International Review on Modelling and Simulations, 5(6), 2501–2506.

    Google Scholar 

  • Torres-Jimenez, J., & Rodriguez-Cristerna, A. (2017). Metaheuristic post-optimization of the NIST repository of covering arrays. CAAI Transactions on Intelligence Technology, 2(1), 31–38.

    Google Scholar 

  • Wang, Y., & Witten, I. H. (1997). Induction of model trees for predicting continuous classes. In Proceedings of the 9th European conference on machine learning poster papers, Prague (pp. 128–137).

  • Xu, C., Gordan, B., Koopialipoor, M., Armaghani, D. J., Tahir, M. M., & Zhang, X. (2019). Improving performance of retaining walls under dynamic conditions developing an optimized ANN based on ant colony optimization technique. IEEE Access, 7, 94692–94700.

    Google Scholar 

  • Yang, H., Koopialipoor, M., Armaghani, D. J., Gordan, B., Khorami, M., & Tahir, M. M. (2019). Intelligent design of retaining wall structures under dynamic conditions. Steel and Composite Structures, 31(6), 629–640.

    Google Scholar 

  • Yazdani Bejarbaneh, B., Armaghani, D. J., & Mohd Amin, M. F. (2015). Strength characterisation of shale using Mohr–Coulomb and Hoek–Brown criteria. Measurement Journal of the International Measurement Confederation. https://doi.org/10.1016/j.measurement.2014.12.029.

    Article  Google Scholar 

  • Zhou, J., Bejarbaneh, B. Y., Armaghani, D. J., & Tahir, M. M. (2020a). Forecasting of TBM advance rate in hard rock condition based on artificial neural network and genetic programming techniques. Bulletin of Engineering Geology and the Environment, 79, 2069–2084.

    Google Scholar 

  • Zhou, J., Guo, H., Koopialipoor, M., Armaghani, D. J., & Tahir, M. M. (2020b). Investigating the effective parameters on the risk levels of rockburst phenomena by developing a hybrid heuristic algorithm. Engineering with Computers. https://doi.org/10.1007/s00366-019-00908-9.

    Article  Google Scholar 

  • Zhou, J., Koopialipoor, M., Li, E., & Armaghani, D. J. (2020c). Prediction of rockburst risk in underground projects developing a neuro-bee intelligent system. Bulletin of Engineering Geology and the Environment. https://doi.org/10.1007/s10064-020-01788-w.

  • Zhou, J., Li, C., Koopialipoor, M., Armaghani, D. J., & Thai Pham, B. (2020d). Development of a new methodology for estimating the amount of PPV in surface mines based on prediction and probabilistic models (GEP-MC). International Journal of Mining, Reclamation and Environment. https://doi.org/10.1080/17480930.2020.1734151.

    Article  Google Scholar 

  • Zhou, J., Li, X., & Mitri, H. S. (2015). Comparative performance of six supervised learning methods for the development of models of hard rock pillar stability prediction. Natural Hazards, 79(1), 291–316.

    Google Scholar 

  • Zhou, J., Li, E., Yang, S., Wang, M., Shi, X., Yao, S., et al. (2019). Slope stability prediction for circular mode failure using gradient boosting machine approach based on an updated database of case histories. Safety Science, 118, 505–518.

    Google Scholar 

  • 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. Journal of Vibration and Control, 22(19), 3986–3997.

    Google Scholar 

Download references

Acknowledgments

Authors acknowledge Prof. Dr. Edy Tonnizam Mohamad, Director—Geotropik, Centre of Geoengineering, Universiti Teknologi Malaysia, for provision of data to this study and for encouragement given thorough out the study.

Author information

Authors and Affiliations

  1. Geotropik- Centre of Tropical Geoengineering, School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, 81310, Skudai, Johor, Malaysia

    Bhatawdekar Ramesh Murlidhar & Edy Tonnizam Mohamad

  2. Department of Geotechnics and Transportation, Faculty of Civil Engineering, Universiti Teknologi Malaysia, 81310, Skudai, Johor, Malaysia

    Behnam Yazdani Bejarbaneh

  3. Institute of Research and Development, Duy Tan University, Da Nang, 550000, Vietnam

    Danial Jahed Armaghani

  4. College of Engineering, Civil Engineering Department, University of Sulaimani, Kurdistan Region, Iraq

    Ahmed Salih Mohammed

Authors
  1. Bhatawdekar Ramesh Murlidhar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Behnam Yazdani Bejarbaneh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Danial Jahed Armaghani
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ahmed Salih Mohammed
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Edy Tonnizam Mohamad
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Behnam Yazdani Bejarbaneh or Danial Jahed Armaghani.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ramesh Murlidhar, B., Yazdani Bejarbaneh, B., Jahed Armaghani, D. et al. Application of Tree-Based Predictive Models to Forecast Air Overpressure Induced by Mine Blasting. Nat Resour Res 30, 1865–1887 (2021). https://doi.org/10.1007/s11053-020-09770-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11053-020-09770-9

Keywords

Search

Navigation