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Optimal ELM–Harris Hawks Optimization and ELM–Grasshopper Optimization Models to Forecast Peak Particle Velocity Resulting from Mine Blasting

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Abstract

Most mining and tunneling projects usually require blasting operations to remove rock mass. Previous studies have mentioned that if the blasting operation is not properly designed, it may lead to several environmental issues, such as ground vibration. This study presents various machine learning (ML) techniques, i.e., hybrid extreme learning machines (ELMs) with the grasshopper optimization algorithm (GOA) and Harris hawks optimization (HHO) for controlling and predicting ground vibrations resulting from mine blasting. Actually, the GOA–ELM and HHO–ELM models are improved versions of a previously developed ELM model, and they are able to provide higher performance capacity. For the proposed ML modeling, a database was established consisting of 166 datasets collected from Malaysian quarries. The efficacy of the proposed ML techniques was observed in the training stage as well as in the testing stage, and the results were evaluated against five parameters constituting the fitness criteria. The results showed that the GOA–ELM model delivered more accurate ground vibration values compared to the HHO–ELM model. The system error values of the GOA–ELM model for the training and testing datasets were 2.0239 and 2.8551, respectively. The coefficients of determination of the GOA-ELM model for the training and testing datasets were 0.9410 and 0.9105, respectively. It was concluded that the new hybrid model is able to forecast ground vibration resulting from mine blasting with high level of accuracy. The capabilities of this hybrid model can be extended further to mitigate other environmental issues caused by mine blasting.

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References

  • Aleem, S. H. E. A., Zobaa, A. F., Balci, M. E., & Ismael, S. M. (2019). Harmonic overloading minimization of frequency-dependent components in harmonics polluted distribution systems using harris hawks optimization algorithm. IEEE Access, 7, 100824–100837.

    Google Scholar 

  • Amiri, M., Amnieh, H. B., Hasanipanah, M., & Khanli, L. M. (2016). A new combination of artificial neural network and K-nearest neighbors models to predict blast-induced ground vibration and air-overpressure. Engineering with Computers, 32(4), 631–644.

    Google Scholar 

  • Armaghani, D., Momeni, E., & Abad, S. (2015). Feasibility of ANFIS model for prediction of ground vibrations resulting from quarry blasting. Environmental Earth Sciences, 74, 2845–2860.

    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, D. J., Koopialipoor, M., Bahri, M., Hasanipanah, M., & Tahir, M. M. (2020). 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 

  • Asteris, P. G., Douvika, M. G., Karamani, C. A., Skentou, A. D., Chlichlia, K., Cavaleri, L., et al. (2020). A novel heuristic algorithm for the modeling and risk assessment of the COVID-19 pandemic phenomenon. Computer Modeling in Engineering & Sciences. https://doi.org/10.32604/cmes.2020.013280.

    Article  Google Scholar 

  • Bao, X., Jia, H., & Lang, C. (2019). A novel hybrid Harris hawks optimization for color image multilevel thresholding segmentation. IEEE Access, 7, 76529–76546.

    Google Scholar 

  • Bui, D. T., Moayedi, H., Kalantar, B., Osouli, A., Pradhan, B., Nguyen, H., & Rashid, A. S. A. (2019). A novel swarm intelligence—Harris hawks optimization for spatial assessment of landslide susceptibility. Sensors, 19(16), 3590.

    Google Scholar 

  • Cai, M., Koopialipoor, M., Armaghani, D. J., & Thai Pham, B. (2020). Evaluating slope deformation of earth dams due to earthquake shaking using MARS and GMDH techniques. Applied Sciences, 10(4), 1486.

    Google Scholar 

  • Cao, J., Lin, Z., & Huang, G.-B. (2012). Self-adaptive evolutionary extreme learning machine. Neural Processing Letters, 36(3), 285–305.

    Google Scholar 

  • Cui, D., Huang, G.-B., & Liu, T. (2018). ELM based smile detection using distance vector. Pattern Recognition, 79, 356–369.

    Google Scholar 

  • Davies, B., Farmer, I. W., & Attewell, P. B. (1964). Ground vibration from shallow sub-surface blasts. Engineer, 217(5644), 553–559.

    Google Scholar 

  • Dindarloo, S. R. (2015). Prediction of blast-induced ground vibrations via genetic programming. International Journal of Mining Science and Technology, 25(6), 1011–1015. https://doi.org/10.1016/j.ijmst.2015.09.020.

    Article  Google Scholar 

  • Du, P., Wang, J., Hao, Y., Niu, T., & Yang, W. (2019). A novel hybrid model based on multi-objective Harris hawks optimization algorithm for daily PM2. 5 and PM10 forecasting. arXiv:1905.13550.

  • Duvall, W. I., & Petkof, B. (1959). Spherical propagation of explosion-generated strain pulses in rock. USBM Report of Investigation. (Vol. 5483, p. 21).

  • Duvall, W. I., & Fogelson, D. E. (1962). Review of criteria for estimating damage to residences from blasting vibrations. USBM, RI (Vol. 5968, p. 19).

  • Edwards, A. T., & Northwood, T. D. (1960). Experimental studies of the effects of blasting on structures. Washington: Division of Building Research, National Research Council.

    Google Scholar 

  • Guo, H., Zhou, J., Koopialipoor, M., Armaghani, D. J., & Tahir, M. M. (2019). Deep neural network and whale optimization algorithm to assess flyrock induced by blasting. Engineering with Computers. https://doi.org/10.1007/s00366-019-00816-y.

    Article  Google Scholar 

  • Heidari, A. A., Mirjalili, S., Faris, H., Aljarah, I., Mafarja, M., & Chen, H. (2019). Harris hawks optimization: Algorithm and applications. Future Generation Computer Systems, 97, 849–872.

    Google Scholar 

  • Huang, G.-B., Chen, L., & Siew, C. K. (2006). Universal approximation using incremental constructive feedforward networks with random hidden nodes. IEEE Transactions on Neural Networks, 17(4), 879–892.

    Google Scholar 

  • Huang, G.-B., Zhou, H., Ding, X., & Zhang, R. (2011). Extreme learning machine for regression and multiclass classification. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 42(2), 513–529.

    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 

  • Huang, J., Duan, T., Zhang, Y., Liu, J., Zhang, J., & Lei, Y. (2020). Predicting the permeability of pervious concrete based on the beetle antennae search algorithm and random forest model. Advances in Civil Engineering. https://doi.org/10.1155/2020/8863181.

    Article  Google Scholar 

  • Huang, J., Kumar, G. S., & Sun, Y. (2021a) Evaluation of workability and mechanical properties of asphalt binder and  mixture modified with waste toner. Construction and Building Materials, 276, 122230. https://doi.org/10.1016/j.conbuildmat.2020.122230.

    Article  Google Scholar 

  • Huang, J., Zhang, J., Ren, J., & Chen, H. (2021b) Anti-rutting performance of the damping asphalt mixtures (DAMs) made with a high content of asphalt rubber (AR). Construction and Building Materials, 271, 121878. https://doi.org/10.1016/j.conbuildmat.2020.121878.

    Article  Google Scholar 

  • Jia, H., Li, Y., Lang, C., Peng, X., Sun, K., & Li, J. (2019). Hybrid grasshopper optimization algorithm and differential evolution for global optimization. Journal of Intelligent & Fuzzy Systems, 37(5), 6899–6910.

    Google Scholar 

  • Khandelwal, M., Kumar, D. L., & Yellishetty, M. (2011). Application of soft computing to predict blast-induced ground vibration. Engineering with Computers, 27(2), 117–125.

    Google Scholar 

  • Khandelwal, M., & Singh, T. N. (2007). Evaluation of blast-induced ground vibration predictors. Soil Dynamics and Earthquake Engineering, 27(2), 116–125.

    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 

  • Koopialipoor, M., Armaghani, D. J., Hedayat, A., Marto, A., & Gordan, B. (2018a). Applying various hybrid intelligent systems to evaluate and predict slope stability under static and dynamic conditions. Soft Computing. https://doi.org/10.1007/s00500-018-3253-3.

    Article  Google Scholar 

  • Koopialipoor, M., Fahimifar, A., Ghaleini, E. N., Momenzadeh, M., & Armaghani, D. J. (2019a). Development of a new hybrid ANN for solving a geotechnical problem related to tunnel boring machine performance. Engineering with Computers. https://doi.org/10.1007/s00366-019-00701-8.

    Article  Google Scholar 

  • Koopialipoor, M., Fallah, A., Armaghani, D. J., Azizi, A., & Mohamad, E. T. (2018b). Three hybrid intelligent models in estimating flyrock distance resulting from blasting. Engineering with Computers. https://doi.org/10.1007/s00366-018-0596-4.

    Article  Google Scholar 

  • Koopialipoor, M., Ghaleini, E. N., Haghighi, M., Kanagarajan, S., Maarefvand, P., & Mohamad, E. T. (2018c). Overbreak prediction and optimization in tunnel using neural network and bee colony techniques. Engineering with Computers. https://doi.org/10.1007/s00366-018-0658-7.

    Article  Google Scholar 

  • Koopialipoor, M., Ghaleini, E. N., Tootoonchi, H., Jahed Armaghani, D., Haghighi, M., & Hedayat, A. (2019b). Developing a new intelligent technique to predict overbreak in tunnels using an artificial bee colony-based ANN. Environmental Earth Sciences, 78(5), 165. https://doi.org/10.1007/s12665-019-8163-x.

    Article  Google Scholar 

  • Koopialipoor, M., Murlidhar, B. R., Hedayat, A., Armaghani, D. J., Gordan, B., & Mohamad, E. T. (2019c). The use of new intelligent techniques in designing retaining walls. Engineering with Computers. https://doi.org/10.1007/s00366-018-00700-1.

    Article  Google Scholar 

  • Koopialipoor, M., Nikouei, S. S., Marto, A., Fahimifar, A., Armaghani, D. J., & Mohamad, E. T. (2018d). 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., Jahed Armaghani, D., & Yagiz, S. (2019d). 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 

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

    Google Scholar 

  • Li, L.-L., Sun, J., Tseng, M.-L., & Li, Z.-G. (2019). Extreme learning machine optimized by whale optimization algorithm using insulated gate bipolar transistor module aging degree evaluation. Expert Systems with Applications, 127, 58–67.

    Google Scholar 

  • Liu, B., Yang, H., & Karekal, S. (2019). Effect of water content on argillization of mudstone during the tunnelling process. Rock Mechanics and Rock Engineering. https://doi.org/10.1007/s00603-019-01947-w.

    Article  Google Scholar 

  • Lu, S., Koopialipoor, M., Asteris, P. G., Bahri, M., & Armaghani, D. J. (2020). A novel feature selection approach based on tree models for evaluating the punching shear capacity of steel fiber-reinforced concrete flat slabs. Materials, 13(17), 3902.

    Google Scholar 

  • Mafarja, M., Aljarah, I., Faris, H., Hammouri, A. I., Ala’M, A.-Z., & Mirjalili, S. (2019). Binary grasshopper optimisation algorithm approaches for feature selection problems. Expert Systems with Applications, 117, 267–286.

    Google Scholar 

  • Mahdiyar, A., Jahed Armaghani, D., 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 

  • Mohapatra, P., Chakravarty, S., & Dash, P. K. (2015). An improved cuckoo search based extreme learning machine for medical data classification. Swarm and Evolutionary Computation, 24, 25–49.

    Google Scholar 

  • Monjezi, M., Ghafurikalajahi, M., & Bahrami, A. (2011). Prediction of blast-induced ground vibration using artificial neural networks. Tunnelling and Underground Space Technology, 26(1), 46–50. https://doi.org/10.1016/j.tust.2010.05.002.

    Article  Google Scholar 

  • 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 Computing and Applications, 22(7–8), 1637–1643.

    Google Scholar 

  • Nguyen, H., Drebenstedt, C., Bui, X.-N., & Bui, D. T. (2019). Prediction of blast-induced ground vibration in an open-pit mine by a novel hybrid model based on clustering and artificial neural network. Natural Resources Research. https://doi.org/10.1007/s11053-019-09470-z.

    Article  Google Scholar 

  • Nguyen, H., Bui, N. X., Tran, H. Q., & Le, G. H. T. (2020). A novel soft computing model for predicting blast - induced ground vibration in open - pit mines using gene expression programming (in Vietnames). Journal of Mining and Earth Sciences, 61(5), 107–116. https://doi.org/10.46326/jmes.ktlt2020.09.

    Article  Google Scholar 

  • Pal, M., & Deswal, S. (2014). Extreme learning machine based modeling of resilient modulus of subgrade soils. Geotechnical and Geological Engineering, 32(2), 287–296.

    Google Scholar 

  • Pham, T. K., Nguyen, T. D., Le, C. V., & Nguyen, T. V. (2020). Analyzing customer sentiments using K-means algorithm (in Vietnames). Journal of Mining and Earth Sciences, 61(5), 145–150. https://doi.org/10.46326/JMES.KTQTKD2020.19.

    Article  Google Scholar 

  • Qi, C. (2020). Big data management in the mining industry. International Journal of Minerals, Metallurgy and Materials, 27(2), 131–139.

    Google Scholar 

  • Raina, A. K., Haldar, A., Chakraborty, A. K., Choudhury, P. B., Ramulu, M., & Bandyopadhyay, C. (2004). Human response to blast-induced vibration and air-overpressure: an Indian scenario. Bulletin of Engineering Geology and the Environment, 63(3), 209–214.

    Google Scholar 

  • Roy, P. (1993). Putting ground vibration predictions into practice. Colliery Guardian, 241(2), 63–67.

    Google Scholar 

  • 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. Journal of Rock Mechanics and Geotechnical Engineering. https://doi.org/10.1016/j.jrmge.2013.11.001.

    Article  Google Scholar 

  • Saremi, S., Mirjalili, S., & Lewis, A. (2017). Grasshopper optimisation algorithm: Theory and application. Advances in Engineering Software, 105, 30–47.

    Google Scholar 

  • Satapathy, P., Dhar, S., & Dash, P. K. (2017). An evolutionary online sequential extreme learning machine for maximum power point tracking and control in multi-photovoltaic microgrid system. Renewable Energy Focus, 21, 33–53.

    Google Scholar 

  • Shahnazar, A., Nikafshan Rad, H., Hasanipanah, M., Tahir, M. M., Jahed Armaghani, D., & Ghoroqi, M. (2017). A new developed approach for the prediction of ground vibration using a hybrid PSO-optimized ANFIS-based model. Environmental Earth Sciences. https://doi.org/10.1007/s12665-017-6864-6.

    Article  Google Scholar 

  • Shang, Y., Nguyen, H., Bui, X.-N., Tran, Q.-H., & Moayedi, H. (2020). A novel artificial intelligence approach to predict blast-induced ground vibration in open-pit mines based on the firefly algorithm and artificial neural network. Natural Resources Research, 29, 723–737. https://doi.org/10.1007/s11053-019-09503-7

    Article  Google Scholar 

  • Sheykhi, H., Bagherpour, R., Ghasemi, E., & Kalhori, H. (2018). Forecasting ground vibration due to rock blasting: A hybrid intelligent approach using support vector regression and fuzzy C-means clustering. Engineering with Computers, 34(2), 357–365.

    Google Scholar 

  • Standard, I. (1973). Criteria for safety and design of structures subjected to underground blast. ISI, IS-6922.

  • 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.

    Article  Google Scholar 

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

    Google Scholar 

  • Verma, A. K., & Singh, T. N. (2011). Intelligent systems for ground vibration measurement: a comparative study. Engineering with Computers, 27(3), 225–233.

    Google Scholar 

  • 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. Q., Li, Z., Jie, T. Q., & Zhang, Z. Q. (2018a). Effects of joints on the cutting behavior of disc cutter running on the jointed rock mass. Tunnelling and Underground Space Technology, 81, 112–120.

    Google Scholar 

  • Yang, H. Q., Xing, S. G., Wang, Q., & Li, Z. (2018b). Model test on the entrainment phenomenon and energy conversion mechanism of flow-like landslides. Engineering Geology, 239, 119–125.

    Google Scholar 

  • Ye, J., Koopialipoor, M., Zhou, J., Armaghani, D. J., & He, X. (2020). A novel combination of tree-based modeling and monte carlo simulation for assessing risk levels of flyrock induced by mine blasting. Natural Resources Research. https://doi.org/10.1007/s11053-020-09730-3.

    Article  Google Scholar 

  • Zhao, X., Fourie, A., & Qi, C. (2019). An analytical solution for evaluating the safety of an exposed face in a paste backfill stope incorporating the arching phenomenon. International Journal of Minerals, Metallurgy, and Materials, 26(10), 1206–1216.

    Google Scholar 

  • Zhao, X., Fourie, A., & Qi, C. (2020a). Mechanics and safety issues in tailing-based backfill: A review. International Journal of Minerals, Metallurgy and Materials, 27(9), 1165–1178.

    Google Scholar 

  • Zhao, X., Fourie, A., Veenstra, R., & Qi, C. (2020b). Safety of barricades in cemented paste-backfilled stopes. International Journal of Minerals, Metallurgy and Materials, 27(8), 1054–1064.

    Google Scholar 

  • Zhou, J., Guo, H., Koopialipoor, M., Armaghani, D. J., & Tahir, M. M. (2020a). 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.

    Article  Google Scholar 

  • Zhou, J., Koopialipoor, M., Murlidhar, B. R., Fatemi, S. A., Tahir, M. M., Armaghani, D. J., & Li, C. (2019). Use of intelligent methods to design effective pattern parameters of mine blasting to minimize flyrock distance. Natural Resources Research. https://doi.org/10.1007/s11053-019-09519-z.

    Article  Google Scholar 

  • Zhou, J., Li, C., Koopialipoor, M., Jahed Armaghani, D., & Thai Pham, B. (2020). 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 

  • Zhu, H., Tsang, E. C. C., & Zhu, J. (2018). Training an extreme learning machine by localized generalization error model. Soft Computing, 22(11), 3477–3485.

    Google Scholar 

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Authors and Affiliations

  1. China Railway 18Th Bureau Group No.2 Engineering Co., Ltd., Hebei, 064000, China

    Canxin Yu & Zengli Wang

  2. Faculty of Civil and Environmental Engineering, Amirkabir University of Technology, 15914, Tehran, Iran

    Mohammadreza Koopialipoor

  3. Centre of Tropical Geoengineering (GEOTROPIK), School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, 81310, Johor Bahru, Malaysia

    Bhatawdekar Ramesh Murlidhar & Edy Tonnizam Mohamad

  4. Department of Mining Engineering, Indian Institute of Technology, Kharagpur, India

    Bhatawdekar Ramesh Murlidhar

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

    Ahmed Salih Mohammed

  6. Scientific Research and Development Center, Nawroz University, Duhok, Kurdistan Region, Iraq

    Ahmed Salih Mohammed

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

    Danial Jahed Armaghani

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  1. Canxin Yu
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Correspondence to Mohammadreza Koopialipoor or Danial Jahed Armaghani.

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Yu, C., Koopialipoor, M., Murlidhar, B.R. et al. Optimal ELM–Harris Hawks Optimization and ELM–Grasshopper Optimization Models to Forecast Peak Particle Velocity Resulting from Mine Blasting. Nat Resour Res 30, 2647–2662 (2021). https://doi.org/10.1007/s11053-021-09826-4

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