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Predicting Blast-Induced Ground Vibration in Open-Pit Mines Using Different Nature-Inspired Optimization Algorithms and Deep Neural Network

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Abstract

Blast-induced ground vibration (GV) is a hazardous phenomenon in open-pit mines, and it has unquestionable effects, such as slope instability, deformation of structures, and changing the flow direction of groundwater. Therefore, many studies in recent years have focused on the accurate prediction and control of GV in open-pit mines. In this study, three intelligent hybrid models were examined for predicting GV based on different nature-inspired optimization algorithms and deep neural networks. Accordingly, a deep neural network (DNN) was developed for predicting GV under the enhancement of deep learning techniques. Subsequently, aiming at improving the accuracy and reducing the error of the DNN model in terms of the prediction of blast-induced GVs, three optimization algorithms based on the behaviors of whale, Harris hawks, and particle swarm in nature (abbreviated as WOA, HHOA, and PSOA, respectively) were considered and applied, namely HHOA–DNN, WOA–DNN, and PSOA–DNN, respectively. The results were then compared with those of the conventional DNN model through various performance indices; 229 blasting events in an open-pit coal mine in Vietnam were processed for this aim. Finally, it was found that the proposed intelligent hybrid models outperform the DNN model with deep learning techniques, although it is a state-of-the-art model that has been recommended and claimed by previous researchers. In particular, HHOA, WOA, and PSOA (with global optimization) further improved the accuracy of the DNN model by 1–2%. Of those, the HHOA–DNN model provided the highest performance with a mean-squared-error of 2.361, root mean squared error of 1.537, mean absolute percentage error of 0.123, variance accounted for of 93.015, and coefficient determination of 0.930 on the testing dataset. The findings also revealed that the explosive charge per blast, monitoring distance, and time delay per each blasting group are necessary parameters for predicting GV.

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References

  • Afeni, T. B. (2009). Optimization of drilling and blasting operations in an open pit mine—The SOMAIR experience. Mining Science and Technology (china), 19(6), 736–739.

    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 

  • Amiri, M., Hasanipanah, M., & Bakhshandeh Amnieh, H. (2020). Predicting ground vibration induced by rock blasting using a novel hybrid of neural network and itemset mining. Neural Computing and Applications, 32, 14681–14699 (2020). https://doi.org/10.1007/s00521-020-04822-w

  • Armaghani, D. J., Hasanipanah, M., Amnieh, H. B., & Mohamad, E. T. (2018). Feasibility of ICA in approximating ground vibration resulting from mine blasting. Neural Computing and Applications, 29(9), 457–465.

    Google Scholar 

  • Jahed Armaghani, D., Kumar, D., Samui, P., Hasanipanah, M., & Roy, B. (2020). A novel approach for forecasting of ground vibrations resulting from blasting: Modified particle swarm optimization coupled extreme learning machine. Engineering with Computers. https://doi.org/10.1007/s00366-020-00997-x

  • Armaghani, D. J., Mahdiyar, A., Hasanipanah, M., Faradonbeh, R. S., Khandelwal, M., & Amnieh, H. B. (2016). 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), 3631–3641.

    Google Scholar 

  • Armaghani, D. J., Momeni, E., Abad, S. V. A. N. K., & Khandelwal, M. (2015). Feasibility of ANFIS model for prediction of ground vibrations resulting from quarry blasting. Environmental Earth Sciences, 74(4), 2845–2860.

    Google Scholar 

  • Arthur, C. K., Temeng, V. A., & Ziggah, Y. Y. (2020). Novel approach to predicting blast-induced ground vibration using Gaussian process regression. Engineering with Computers, 36(1), 29–42.

    Google Scholar 

  • Bairathi D., & Gopalani D. (2020). A novel swarm intelligence based optimization method: Harris’ hawk optimization. In: A. Abraham, A. Cherukuri, P. Melin, & N. Gandhi (Eds.), Intelligent systems design and applications. ISDA 2018. Advances in Intelligent Systems and Computing (Vol. 941). Springer, Cham. https://doi.org/10.1007/978-3-030-16660-1_81

  • Bayat, P., Monjezi, M., Rezakhah, M., & Jahed Armaghani, D. (2020). Artificial neural network and firefly algorithm for estimation and minimization of ground vibration induced by blasting in a mine. Natural Resources Research, 29, 4121–4132. https://doi.org/10.1007/s11053-020-09697-1

  • Beşkirli, A., & Dağ, İ. (2020). A new binary variant with transfer functions of Harris Hawks Optimization for binary wind turbine micrositing. Energy Reports, 6, 668–673.

    Google Scholar 

  • Bisoyi, S., & Pal, B. (2020). Prediction of ground vibration using various regression analysis. Journal of Mining Science, 56(3), 378–387.

    Google Scholar 

  • Bratton, D., & Kennedy, J. (2007). Defining a standard for particle swarm optimization. In: 2007 IEEE Swarm Intelligence Symposium (pp. 120–127). https://doi.org/10.1109/SIS.2007.368035

  • Brownlee, J. (2018). Deep learning for time series forecasting: Predict the future with MLPs, CNNs and LSTMs in Python. Machine Learning Mastery.

    Google Scholar 

  • Cao, H., Qian, X., Chen, Z., & Zhu, H. (2017). Layout and size optimization of suspension bridges based on coupled modelling approach and enhanced particle swarm optimization. Engineering Structures, 146, 170–183.

    Google Scholar 

  • Chen, H., Li, W., & Yang, X. (2020). A whale optimization algorithm with chaos mechanism based on quasi-opposition for global optimization problems. Expert Systems with Applications, 158, 113612.

    Google Scholar 

  • Clerc, M. (2010). Particle swarm optimization (Vol. 93). Wiley.

    Google Scholar 

  • Ding, X., Hasanipanah, M., Nikafshan Rad, H., & Zhou, W. (2021). Predicting the blast-induced vibration velocity using a bagged support vector regression optimized with firefly algorithm. Engineering with Computers, 37, 2273–2284. https://doi.org/10.1007/s00366-020-00937-9

  • Du, K. L., & Swamy, M. N. S. (2016). Particle swarm optimization. In: Search and optimization by metaheuristics. Birkhäuser, Cham. https://doi.org/10.1007/978-3-319-41192-7_9

  • Duvall, W. I., & Petkof, B. (1959). Spherical propagation of explosion-generated strain pulses in rock. US Department of the Interior, Bureau of Mines. Report of Investigations 5483.

  • Faradonbeh, A., Majid, T., Murlidhar, M., et al. (2016). Prediction of ground vibration due to quarry blasting based on gene expression programming: A new model for peak particle velocity prediction. International Journal of Environmental Science and Technology, 13(6), 1453–1464.

    Google Scholar 

  • Faramarzi, F., Farsangi, M. A. E., & Mansouri, H. (2014). Simultaneous investigation of blast induced ground vibration and airblast effects on safety level of structures and human in surface blasting. International Journal of Mining Science and Technology, 24(5), 663–669.

    Google Scholar 

  • Fattahi, H., & Hasanipanah, M. (2021). Prediction of blast-induced ground vibration in a mine using relevance vector regression optimized by metaheuristic algorithms. Natural Resources Research, 30, 1849–1863. https://doi.org/10.1007/s11053-020-09764-7

  • Garcia-Garcia, A., Orts-Escolano, S., Oprea, S., Villena-Martinez, V., Martinez-Gonzalez, P., & Garcia-Rodriguez, J. (2018). A survey on deep learning techniques for image and video semantic segmentation. Applied Soft Computing, 70, 41–65.

    Google Scholar 

  • Ghasemi, E., Sari, M., & Ataei, M. (2012). Development of an empirical model for predicting the effects of controllable blasting parameters on flyrock distance in surface mines. International Journal of Rock Mechanics and Mining Sciences, 52, 163–170.

    Google Scholar 

  • Gölcük, İ., & Ozsoydan, F. B. (2021). Quantum particles-enhanced multiple Harris Hawks swarms for dynamic optimization problems. Expert Systems with Applications, 167, 114202.

  • Goodfellow, I., Bengio, Y., Courville, A., & Bengio, Y. (2016). Deep learning. MIT Press Publisher. http://www.deeplearningbook.org

  • Guo, H., Nguyen, H., Bui, X.-N., & Armaghani, D. J. (2019). A new technique to predict fly-rock in bench blasting based on an ensemble of support vector regression and GLMNET. Engineering with Computers. https://doi.org/10.1007/s00366-019-00833-x

    Article  Google Scholar 

  • Hajihassani, M., Armaghani, D. J., Marto, A., & Mohamad, E. T. (2015). Ground vibration prediction in quarry blasting through an artificial neural network optimized by imperialist competitive algorithm. Bulletin of Engineering Geology and the Environment, 74(3), 873–886.

    Google Scholar 

  • Han, J., Zhang, D., Cheng, G., Liu, N., & Xu, D. (2018). Advanced deep-learning techniques for salient and category-specific object detection: A survey. IEEE Signal Processing Magazine, 35(1), 84–100.

    Google Scholar 

  • Hasanipanah, M., Golzar, S. B., Larki, I. A., Maryaki, M. Y., & Ghahremanians, T. (2017). Estimation of blast-induced ground vibration through a soft computing framework. Engineering with Computers, 33(4), 951–959.

    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 

  • 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 

  • Himanshu, V. K., Roy, M., Mishra, A., Paswan, R. K., Panda, D., & Singh, P. (2018). Multivariate statistical analysis approach for prediction of blast-induced ground vibration. Arabian Journal of Geosciences, 11(16), 1–11.

    Google Scholar 

  • Huang, J., Koopialipoor, M., & Armaghani, D. J. (2020). A combination of fuzzy Delphi method and hybrid ANN-based systems to forecast ground vibration resulting from blasting. Scientific Reports, 10(1), 1–21.

    Google Scholar 

  • Kahriman, A. (2002). Analysis of ground vibrations caused by bench blasting at can open-pit lignite mine in Turkey. Environmental Geology, 41(6), 653–661.

    Google Scholar 

  • Kahriman, A., Ozer, U., Aksoy, M., Karadogan, A., & Tuncer, G. (2006). Environmental impacts of bench blasting at Hisarcik Boron open pit mine in Turkey. Environmental Geology, 50(7), 1015–1023.

    Google Scholar 

  • Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. In: Proceedings of ICNN'95 - International Conference on Neural Networks (Vol. 4, pp. 1942–1948). https://doi.org/10.1109/ICNN.1995.488968

  • Kiranyaz, S., Ince, T., & Gabbouj, M. (2014). Multidimensional particle swarm optimization for machine learning and pattern recognition. Springer.

    Google Scholar 

  • Krithiga, R., & Ilavarasan, E. (2020). A reliable modified whale optimization algorithm based approach for feature selection to classify twitter spam profiles. Microprocessors and Microsystems. https://doi.org/10.1016/j.micpro.2020.103451

    Article  Google Scholar 

  • Kumar, R., Choudhury, D., & Bhargava, K. (2016). Determination of blast-induced ground vibration equations for rocks using mechanical and geological properties. Journal of Rock Mechanics and Geotechnical Engineering, 8(3), 341–349.

    Google Scholar 

  • Lawal, A. I., Kwon, S., & Kim, G. Y. (2021). Prediction of the blast-induced ground vibration in tunnel blasting using ANN, moth-flame optimized ANN, and gene expression programming. Acta Geophysica, 69, 161–174.

    Google Scholar 

  • Lazinica, A. (2009). Particle swarm optimization. BoD-Books on Demand. IntechOpen.

  • Li, A.-D., & He, Z. (2020). Multiobjective feature selection for key quality characteristic identification in production processes using a nondominated-sorting-based whale optimization algorithm. Computers & Industrial Engineering, 149, 106852.

    Google Scholar 

  • Liu, H., & Cocea, M. (2017). Semi-random partitioning of data into training and test sets in granular computing context. Granular Computing, 2(4), 357–386.

    Google Scholar 

  • Mahdiyar, A., Marto, A., & Mirhosseinei, S. A. (2018). Probabilistic air-overpressure simulation resulting from blasting operations. Environmental Earth Sciences, 77(4), 123.

    Google Scholar 

  • Maleki, A., Haghighi, A., Irandoost Shahrestani, M., & Abdelmalek, Z. (2021). Applying different types of artificial neural network for modeling thermal conductivity of nanofluids containing silica particles. Journal of Thermal Analysis and Calorimetry, 144, 1613–1622. https://doi.org/10.1007/s10973-020-09541-x

  • Mirjalili, S., & Lewis, A. (2016). The whale optimization algorithm. Advances in Engineering Software, 95, 51–67.

    Google Scholar 

  • Moayedi, H., Osouli, A., Nguyen, H., & Rashid, A. S. A. (2021). A novel Harris hawks’ optimization and k-fold cross-validation predicting slope stability. Engineering with Computers, 37, 369–379. https://doi.org/10.1007/s00366-019-00828-8

  • Mohammadi, D., Mikaeil, R., & Abdollahi-Sharif, J. (2020). Implementation of an optimized binary classification by GMDH-type neural network algorithm for predicting the blast produced ground vibration. Expert Systems, 37(5), e12563.

    Google Scholar 

  • Molaei, S., Moazen, H., Najjar-Ghabel, S., & Farzinvash, L. (2021). Particle swarm optimization with an enhanced learning strategy and crossover operator. Knowledge-Based Systems. https://doi.org/10.1016/j.knosys.2021.106768

    Article  Google Scholar 

  • Monjezi, M., Bahrami, A., Varjani, A. Y., & Sayadi, A. R. (2011). Prediction and controlling of flyrock in blasting operation using artificial neural network. Arabian Journal of Geosciences, 4(3–4), 421–425.

    Google Scholar 

  • Munagala, V. K., & Jatoth, R. K. (2021). Design of fractional-order PID/PID controller for speed control of DC motor using harris hawks optimization. In: R. Kumar, V. P. Singh, A. Mathur (Eds.) Intelligent algorithms for analysis and control of dynamical systems. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-15-8045-1_11

  • Murlidhar, B. R., Armaghani, D. J., & Mohamad, E. T. (2020). Intelligence prediction of some selected environmental issues of blasting: A review. The Open Construction & Building Technology Journal, 14, 298–308. https://doi.org/10.2174/1874836802014010298

  • Nateghi, R. (2011). Prediction of ground vibration level induced by blasting at different rock units. International Journal of Rock Mechanics and Mining Sciences, 48(6), 899–908.

    Google Scholar 

  • Nguyen, H., Bui, X.-N., Bui, H.-B., & Mai, N.-L. (2018). A comparative study of artificial neural networks in predicting blast-induced air-blast overpressure at Deo Nai open-pit coal mine, Vietnam. Neural Computing and Applications, 32(8), 3939–3955.

    Google Scholar 

  • Nguyen, H., Bui, X.-N., Choi, Y., Lee, C. W., & Armaghani, D. J. (2020). A Novel combination of whale optimization algorithm and support vector machine with different kernel functions for prediction of blasting-induced fly-rock in quarry mines. Natural Resources Research. https://doi.org/10.1007/s11053-020-09710-7

    Article  Google Scholar 

  • Nguyen, H., Bui, X.-N., & Moayedi, H. (2019a). A comparison of advanced computational models and experimental techniques in predicting blast-induced ground vibration in open-pit coal mine. Acta Geophysica, 67(4), 1025–1037.

    Google Scholar 

  • Nguyen, H., Bui, X.-N., Tran, Q.-H., & Mai, N.-L. (2019b). A new soft computing model for estimating and controlling blast-produced ground vibration based on hierarchical K-means clustering and cubist algorithms. Applied Soft Computing, 77, 376–386. https://doi.org/10.1016/j.asoc.2019.01.042

    Article  Google Scholar 

  • Ozer, U., Kahriman, A., Aksoy, M., Adiguzel, D., & Karadogan, A. (2008). The analysis of ground vibrations induced by bench blasting at Akyol quarry and practical blasting charts. Environmental Geology, 54(4), 737–743.

    Google Scholar 

  • Poli, R., Kennedy, J., & Blackwell, T. (2007). Particle swarm optimization. Swarm Intelligence, 1(1), 33–57.

    Google Scholar 

  • Qi, W. (2021). Optimization of cloud computing task execution time and user QoS utility by improved particle swarm optimization. Microprocessors and Microsystems, 80, 103529.

    Google Scholar 

  • Qian, X., Jia, S., Huang, K., Chen, H., Yuan, Y., & Zhang, L. (2020). Optimal design of Kaibel dividing wall columns based on improved particle swarm optimization methods. Journal of Cleaner Production, 273, 123041. https://doi.org/10.1016/j.jclepro.2020.123041

    Article  Google Scholar 

  • Radha, R., & Gopalakrishnan, R. (2020). A medical analytical system using intelligent fuzzy level set brain image segmentation based on improved quantum particle swarm optimization. Microprocessors and Microsystems, 79, 103283.

    Google Scholar 

  • Salman, A. G., Kanigoro, B., & Heryadi, Y. (2015). Weather forecasting using deep learning techniques. In: 2015 international conference on advanced computer science and information systems (ICACSIS) (pp. 281–285). https://doi.org/10.1109/ICACSIS.2015.7415154

  • Senthil Kumar, A., Sudheer, K., Jain, S., & Agarwal, P. (2005). Rainfall-runoff modelling using artificial neural networks: Comparison of network types. Hydrological Processes: An International Journal, 19(6), 1277–1291.

    Google Scholar 

  • Setiawan, I., Yusnitasari, T., Nurhady, H., & Hizviani, N. V. (2020). Implementation of convolutional neural network method for classification of Baum Test. In: 2020 fifth international conference on informatics and computing (ICIC) (pp. 1–6). https://doi.org/10.1109/ICIC50835.2020.9288595

  • Shankar, N., & SaravanaKumar, N. (2020). Reduced partial shading effect in multiple PV array configuration model using MPPT based enhanced particle swarm optimization technique. Microprocessors and Microsystems. https://doi.org/10.1016/j.micpro.2020.103287

    Article  Google Scholar 

  • Singh, A., & Khamparia, A. (2020). A hybrid whale optimization-differential evolution and genetic algorithm based approach to solve unit commitment scheduling problem: WODEGA. Sustainable Computing: Informatics and Systems, 28, 100442.

    Google Scholar 

  • Singh, C. P., Agrawal, H., & Mishra, A. K. (2020). A study on influence of blast-induced ground vibration in dragline bench blasting using signature hole analysis. Arabian Journal of Geosciences, 13(13), 1–9.

    Google Scholar 

  • Sulaiman, N., Taib, M. N., Lias, S., Murat, Z. H., Aris, S. A. M., & Hamid, N. H. A. (2011). EEG-based stress features using spectral centroids technique and k-nearest neighbor classifier. In: 2011 UkSim 13th international conference on computer modelling and simulation (pp. 69–74). https://doi.org/10.1109/UKSIM.2011.23

  • Wang, H., Peng, M.-J., Ayodeji, A., Xia, H., Wang, X.-K., & Li, Z.-K. (2021). Advanced fault diagnosis method for nuclear power plant based on convolutional gated recurrent network and enhanced particle swarm optimization. Annals of Nuclear Energy, 151, 107934.

    Google Scholar 

  • Yan, Z., Zhang, J., Zeng, J., & Tang, J. (2021). Nature-inspired approach: An enhanced whale optimization algorithm for global optimization. Mathematics and Computers in Simulation, 185, 17–46.

    Google Scholar 

  • Yang, H., Hasanipanah, M., Tahir, M. M., & Tien Bui, D. (2020). Intelligent prediction of blasting-induced ground vibration using ANFIS optimized by GA and PSO. Natural Resources Research, 29, 739–750. https://doi.org/10.1007/s11053-019-09515-3

  • Yang, H., Rad, H. N., Hasanipanah, M., Amnieh, H. B., & Nekouie, A. (2020). Prediction of vibration velocity generated in mine blasting using support vector regression improved by optimization algorithms. Natural Resources Research, 29(2), 807–830.

    Google Scholar 

  • Yegnanarayana, B. (2006). Artificial neural networks. PHI Learning Pvt. Ltd. New Delhi.

  • Yu, C., Koopialipoor, M., Murlidhar, B. R. Mohammed, A. S., Armaghani, D. J., Mohamad, E. T., & Wang, Z. (2021). Optimal ELM–harris hawks optimization and ELM–grasshopper optimization models to forecast peak particle velocity resulting from mine blasting. Natural Resources Research, 30, 2647–2662. https://doi.org/10.1007/s11053-021-09826-4

  • Yu, Z., Shi, X., Zhou, J., Gou, Y., Huo, X., Zhang, J., & Armaghani, D. J. (2020). A new multikernel relevance vector machine based on the HPSOGWO algorithm for predicting and controlling blast-induced ground vibration. Engineering with Computers. https://doi.org/10.1007/s00366-020-01136-2

  • Zeng, N., Song, D., Li, H., You, Y., Liu, Y., & Alsaadi, F. E. (2021). A competitive mechanism integrated multi-objective whale optimization algorithm with differential evolution. Neurocomputing, 432, 170–182.

    Google Scholar 

  • Zhang, S., Bui, X.-N., Trung, N.-T., Nguyen, H., & Bui, H.-B. (2020a). Prediction of rock size distribution in mine bench blasting using a novel ant colony optimization-based boosted regression tree technique. Natural Resources Research, 29(2), 867–886.

    Google Scholar 

  • Zhang, Y., Zhou, X., & Shih, P.-C. (2020b). Modified Harris Hawks Optimization algorithm for global optimization problems. Arabian Journal for Science and Engineering, 45(12), 10949–10974.

    Google Scholar 

  • Zhou, J., Asteris, P. G., Armaghani, D. J., & Pham, B. T. (2020). Prediction of ground vibration induced by blasting operations through the use of the Bayesian Network and random forest models. Soil Dynamics and Earthquake Engineering, 139, 106390.

    Google Scholar 

  • Zou J., Han Y., & So, S. S. (2008). Overview of artificial neural networks. In: D. J. Livingstone (Ed.) Artificial neural networks. Methods in Molecular Biology™ (Vol. 458). Humana Press. https://doi.org/10.1007/978-1-60327-101-1_2

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Acknowledgments

This paper was supported by the Ministry of Education and Training (MOET) in Viet Nam under Grant Number B2020-MDA-16. The authors also thank the Center for Mining, Electro-Mechanical research of Hanoi University of Mining and Geology, Hanoi, Vietnam.

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  1. Department of Surface Mining, Mining Faculty, Hanoi University of Mining and Geology, 18 Vien Str., Duc Thang Ward, Bac Tu Liem Dist., Hanoi, 100000, Vietnam

    Hoang Nguyen, Xuan-Nam Bui, Quang-Hieu Tran, Dinh-An Nguyen, Le Thi Thu Hoa & Qui-Thao Le

  2. Innovations for Sustainable and Responsible Mining (ISRM) Group, Hanoi University of Mining and Geology, 18 Vien Str., Duc Thang Ward, Bac Tu Liem Dist., Hanoi, 100000, Vietnam

    Xuan-Nam Bui, Quang-Hieu Tran & Qui-Thao Le

  3. Mathematics Department, Faculty of Sciences, Hanoi University of Mining and Geology, 18 Vien Str., Duc Thang Ward, Bac Tu Liem Dist., Hanoi, 100000, Vietnam

    Le Thi Huong Giang

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Nguyen, H., Bui, XN., Tran, QH. et al. Predicting Blast-Induced Ground Vibration in Open-Pit Mines Using Different Nature-Inspired Optimization Algorithms and Deep Neural Network. Nat Resour Res 30, 4695–4717 (2021). https://doi.org/10.1007/s11053-021-09896-4

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