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A study on environmental issues of blasting using advanced support vector machine algorithms

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A Correction to this article was published on 27 March 2022

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Abstract

Air overpressure is a critical negative effect of blasting in construction or production sites and projects. So far, many attempts have been made to prevent or reduce this negative effect on the nearby construction, equipment, or people. While various experiential equations have been proposed to forecast the air overpressure value for determining the blasting area, these models are typically inaccurate and impractical. Due to the recent efforts to predict the air overpressure by employing artificial intelligence techniques, this study developed five support vector machine-based models optimized by some praised optimization techniques, including the moth flame optimization, particle swarm optimization, grey wolf optimization, cuckoo optimization algorithm, and whale optimization algorithm. These algorithms optimize the most important parameters of the support vector machine, including “C” and “gamma”, and improve the performance of this model for air overpressure prediction. The findings showed that the moth flame optimization algorithm is the best optimizer for support vector machine and is suitable for air overpressure prediction. The support vector machine–moth flame optimization model achieved the best R2 (train: 0.9939; test: 0.9941) and comprehensive score (34). On the other hand, the worst model was the support vector machine–particle swarm optimization, which achieved the lowest comprehensive score (13). In addition, all optimization techniques improved the performance of the single support vector machine model. The findings of this study imply that all optimization techniques successfully enhanced the performance of the support vector machine model; however, the moth flame optimization optimizer was the most effective one. The support vector machine–moth flame optimization technique can be employed to solve other mining-related issues.

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Source: Mirjalili and Lewis (2016)

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Source: Mirjalili (2015)

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Source: Rajabioun (2011)

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References

  • Armaghani DJ, Hajihassani M, Mohamad ET, Marto A, Noorani SA (2014) Blasting-induced flyrock and ground vibration prediction through an expert artificial neural network based on particle swarm optimization. Arab J Geosci 7(12):5383–5396

    Article  Google Scholar 

  • Armaghani DJ, Harandizadeh H, Momeni E, Maizir H, Zhou J (2021) An optimized system of GMDH-ANFIS predictive model by ICA for estimating pile bearing capacity. Artif Intell Rev 54(7):1–38. https://doi.org/10.1007/s10462-021-10065-5

    Article  Google Scholar 

  • Armaghani DJ, Momeni E, Abad SVANK, Khandelwal M (2015) Feasibility of ANFIS model for prediction of ground vibrations resulting from quarry blasting. Environ Earth Sci 74(4):2845–2860. https://doi.org/10.1007/s12665-015-4305-y

    Article  Google Scholar 

  • Asteris PG, Mamou A, Hajihassani M, Hasanipanah M, Koopialipoor M, Le T-T, Kardani N, Armaghani DJ (2021) Soft computing based closed form equations correlating L and N-type Schmidt hammer rebound numbers of rocks. Transp Geotech 29:100588

    Article  Google Scholar 

  • Chen S-Y, Lu C-C (2015) A model of green acceptance and intentions to use bike-sharing: youbike users in Taiwan. Netw Spat Econ 16(4):1103–1124. https://doi.org/10.1007/s11067-015-9312-8

    Article  Google Scholar 

  • Chen S, Zhang Z, Wu J (2015) Human comfort evaluation criteria for blast planning. Environ Earth Sci 74(4):2919–2923

    Article  Google Scholar 

  • Chopra J, Sehgal S (2021) Summarization of Band Selection Methods For Hyperspectral Images. In: 2021 11th international conference on cloud computing, data science & engineering (confluence),

  • Eberhart R, Kennedy J (1995) Particle swarm optimization. In: Proceedings of the IEEE international conference on neural networks

  • Erzin Y, MolaAbasi H, Kordnaeij A, Erzin S (2020) Prediction of compression index of saturated clays using robust optimization model. J Soft Comput Civil Eng 4(3):1–16

    Google Scholar 

  • Fattahi H, Hasanipanah M (2021) Prediction of blast-induced ground vibration in a mine using relevance vector regression optimized by metaheuristic algorithms. Nat Resour Res 30(2):1849–1863

    Article  Google Scholar 

  • Ferdowsi A, Hoseini SM, Farzin S, Faramarzpour M, Mousavi S-F (2020) Shape optimization of gravity dams using a nature-inspired approach. J Soft Comput Civil Eng 4(3):65–78

    Google Scholar 

  • Hajihassani M, Armaghani DJ, Sohaei H, Mohamad ET, Marto A (2014) Prediction of airblast-overpressure induced by blasting using a hybrid artificial neural network and particle swarm optimization. Appl Acoust 80:57–67

    Article  Google Scholar 

  • Harandizadeh H, Armaghani DJ (2021) Prediction of air-overpressure induced by blasting using an ANFIS-PNN model optimized by GA. Appl Soft Comput 99:106904

    Article  Google Scholar 

  • Harati M, Mashayekhi M, Estekanchi HE (2020) Correlation of ground motion duration with its intensity metrics: a simulation based approach. J Soft Comput Civil Eng 4(3):17–39

    Google Scholar 

  • Hasanipanah M, Armaghani DJ, Khamesi H, Amnieh HB, Ghoraba S (2016) Several non-linear models in estimating air-overpressure resulting from mine blasting. Eng Comput 32(3):441–455

    Article  Google Scholar 

  • He Z, Armaghani DJ, Masoumnezhad M, Khandelwal M, Zhou J, Murlidhar BR (2021) 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

    Article  Google Scholar 

  • ISRM (2007) The complete ISRM suggested methods for rock characterization, testing and monitoring: 1974–2006. In: International society for rock mechanics, commission on testing methods

  • Jia H, Ma J, Song W (2019) Multilevel thresholding segmentation for color image using modified moth-flame optimization. IEEE Access 7:44097–44134

    Article  Google Scholar 

  • Kalita DJ, Singh VP, Kumar V (2021) A dynamic framework for tuning SVM hyper parameters based on moth-flame optimization and knowledge-based-search. Exp Syst Appl 168:114139

    Article  Google Scholar 

  • Kaya D (2019) Optimization of SVM parameters with hybrid CS-PSO algorithms for Parkinson’s disease in LabVIEW environment. Parkinson’s Disease

  • Ke B, Nguyen H, Bui X-N, Costache R (2021) Estimation of ground vibration intensity induced by mine blasting using a state-of-the-art hybrid autoencoder neural network and support vector regression model. Natl Resour Res 30(5):3853–3864

    Article  Google Scholar 

  • Khandelwal M, Kankar P (2011) Prediction of blast-induced air overpressure using support vector machine. Arab J Geosci 4(3–4):427–433

    Article  Google Scholar 

  • Konya CJ, Walter EJ (1990) Surface blast design. Prentice Hall

    Google Scholar 

  • Li E, Zhou J, Shi X, Armaghani DJ, Yu Z, Chen X, Huang P (2020) Developing a hybrid model of salp swarm algorithm-based support vector machine to predict the strength of fiber-reinforced cemented paste backfill. Eng Comput 37(4):1–22

    Article  Google Scholar 

  • Luo Z, Bui X-N, Nguyen H, Moayedi H (2021) A novel artificial intelligence technique for analyzing slope stability using PSO-CA model. Eng Comput 37(1):533–544

    Article  Google Scholar 

  • Mirjalili S (2015) Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm. Knowl-Based Syst 89:228–249

    Article  Google Scholar 

  • Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67. https://doi.org/10.1016/j.advengsoft.2016.01.008

    Article  Google Scholar 

  • Mirjalili S, Mirjalili SM, Lewis A (2014) grey wolf optimizer. Adv Eng Softw 69:46–61. https://doi.org/10.1016/j.advengsoft.2013.12.007

    Article  Google Scholar 

  • Mohamad ET, Armaghani DJ, Hasanipanah M, Murlidhar BR, Alel MNA (2016) Estimation of air-overpressure produced by blasting operation through a neuro-genetic technique. Environ Earth Sci 75(2):174

    Article  Google Scholar 

  • Mohamed MT (2011) Performance of fuzzy logic and artificial neural network in prediction of ground and air vibrations. JES J Eng Sci 39(2):425–440

    Google Scholar 

  • Murlidhar BR, Bejarbaneh BY, Armaghani DJ, Mohammed AS, Mohamad ET (2021) Application of tree-based predictive models to forecast air overpressure induced by mine blasting. Nat Resour Res 30(2):1865–1887

    Article  Google Scholar 

  • Naeini EZ, Prindle K (2018) Machine learning and learning from machines. Lead Edge 37(12):886–893

    Article  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. Nat Resour Res 28(3):893–907

    Article  Google Scholar 

  • Nguyen H, Bui X-N, Choi Y, Lee CW, Armaghani DJ (2021) 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. Nat Resour Res 30(1):191–207. https://doi.org/10.1007/s11053-020-09710-7

    Article  Google Scholar 

  • Nguyen H, Choi Y, Bui X-N, Nguyen-Thoi T (2020) Predicting blast-induced ground vibration in open-pit mines using vibration sensors and support vector regression-based optimization algorithms. Sensors 20(1):132

    Article  Google Scholar 

  • Niromand M, Mikaeil R, Advay M (2021) Assessment of the slope stability under geological conditions using FDAHP-TOPSIS (a case study for sungun open pit mine). J Soft Comput Civil Eng 5(4):21–40

    Google Scholar 

  • Parsajoo M, Armaghani DJ, Mohammed AS, Khari M, Jahandari S (2021) Tensile strength prediction of rock material using non-destructive tests: a comparative intelligent study. Transp Geotech 31(1):100652. https://doi.org/10.1016/j.trgeo.2021.100652

    Article  Google Scholar 

  • Rad HN, Hasanipanah M, Rezaei M, Eghlim AL (2018) Developing a least squares support vector machine for estimating the blast-induced flyrock. Eng Comput 34(4):709–717. https://doi.org/10.1007/s00366-017-0568-0

    Article  Google Scholar 

  • Rajabioun R (2011) Cuckoo optimization algorithm. Appl Soft Comput 11(8):5508–5518

    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. Eng Comput 34(2):357–365

    Article  Google Scholar 

  • Shi X-Z, Jian Z, Wu B-B, Huang D, Wei W (2012) Support vector machines approach to mean particle size of rock fragmentation due to bench blasting prediction. Trans Nonferrous Metals Soc China 22(2):432–441. https://doi.org/10.1016/S1003-6326(11)61195-3

    Article  Google Scholar 

  • Shirani Faradonbeh R, Jahed Armaghani D, Abd Majid MZ, Md Tahir M, Ramesh Murlidhar B, Monjezi M, Wong HM (2016) Prediction of ground vibration due to quarry blasting based on gene expression programming: a new model for peak particle velocity prediction. Int J Environ Sci Technol 13(6):1453–1464

    Article  Google Scholar 

  • Temeng VA, Ziggah YY, Arthur CK (2020) A novel artificial intelligent model for predicting air overpressure using brain inspired emotional neural network. Int J Min Sci Technol 30(5):683–689

    Article  Google Scholar 

  • Vapnik V (2013) The nature of statistical learning theory, 2nd edn. Springer science & business media, Berlin

    Google Scholar 

  • Verron S, Tiplica T, Kobi A (2008) Fault detection and identification with a new feature selection based on mutual information. J Process Control 18(5):479–490. https://doi.org/10.1016/j.jprocont.2007.08.003

    Article  CAS  Google Scholar 

  • Yang H, Song K, Zhou J (2022) Automated recognition model of geomechanical information based on operational data of tunneling boring machines. Rock Mech Rock Eng, 1–18

  • Yang H, Wang Z, Song K (2020) A new hybrid grey wolf optimizer-feature weighted-multiple kernel-support vector regression technique to predict TBM performance. Eng Comput. https://doi.org/10.1007/s00366-020-01217-2

    Article  Google Scholar 

  • Yang H, Xing S, Wang Q, Li Z (2018) Model test on the entrainment phenomenon and energy conversion mechanism of flow-like landslides. Eng Geol 239:119–125. https://doi.org/10.1016/j.enggeo.2018.03.023

    Article  Google Scholar 

  • Yang Y, Cho IH (2021) Multiple target machine learning prediction of capacity curves of reinforced concrete shear walls. J Soft Comput Civil Eng 5–4:90–113

    Google Scholar 

  • Ye J, Dalle J, Nezami R, Hasanipanah M, Armaghani DJ (2020) Stochastic fractal search-tuned ANFIS model to predict blast-induced air overpressure. Eng Comput 1–15

  • Zhang X, Nguyen H, Bui X-N, Tran Q-H, Nguyen D-A, Bui DT, Moayedi H (2020) 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

    Article  Google Scholar 

  • Zhou J, Chen C, Wang M, Khandelwal M (2021a) Proposing a novel comprehensive evaluation model for the coal burst liability in underground coal mines considering uncertainty factors. Int J Min Sci Technol. https://doi.org/10.1016/j.ijmst.2021.07.011

    Article  Google Scholar 

  • Zhou J, Qiu Y, Khandelwal M, Zhu S, Zhang X (2021b) Developing a hybrid model of Jaya algorithm-based extreme gradient boosting machine to estimate blast-induced ground vibrations. Int J Rock Mech Min Sci 145:104856. https://doi.org/10.1016/j.ijrmms.2021.104856

    Article  Google Scholar 

  • Zhou J, Qiu Y, Zhu S, Armaghani DJ, Li C, Nguyen H, Yagiz S (2021) Optimization of support vector machine through the use of metaheuristic algorithms in forecasting TBM advance rate. Eng Appl Artif Intell 97:104015

    Article  Google Scholar 

  • Zhou J, Shen X, Qiu Y, Li E, Rao D, Shi X (2021d) Improving the efficiency of microseismic source locating using a heuristic algorithm-based virtual field optimization method. Geomech Geophys Geo-Energy Geo-Resour 7(3):1–18. https://doi.org/10.1007/s40948-021-00285-y

    Article  CAS  Google Scholar 

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Acknowledgements

Supported by the Science and Technology Research Program of Chongqing Municipal Education Commission (Grant No. JQN202103415).

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Authors and Affiliations

  1. Chongqing Vocational Institute of Engineering, Chongqing, 402260, China

    L. Chen

  2. Department of Urban Planning, Engineering Networks and Systems, Institute of Architecture and Construction, South Ural State University, 76, Lenin Prospect, Chelyabinsk, Russia, 454080

    D. J. Armaghani

  3. Faculty of Civil Engineering, Semnan University, Semnan, 3513119111, Iran

    P. Fakharuab

  4. Department of Mining Engineering, Indian Institute of Technology Kharagpur, Kharagpur, 721302, India

    R. M. Bhatawdekar

  5. Geotropik, Centre of Tropical Geoengineering, Department of Civil Engineering, Universiti Teknologi Malaysia, 81310, Johor Bahru, Malaysia

    R. M. Bhatawdekar

  6. Department of Civil Engineering Department, National Institute of Technology Patna, Patna, India

    P. Samui

  7. School of Engineering, Information Technology and Physical Sciences, Federation University Australia, Ballarat, VIC, 3350, Australia

    M. Khandelwal

  8. Department of Civil Engineering, College of Engineering, King Khalid University, Abha, 61421, Saudi Arabia

    K. M. Khedher

  9. Department of Civil Engineering, High Institute of Technological Studies, Mrezgua University Campus, Nabeul, 8000, Tunisia

    K. M. Khedher

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  1. L. Chen
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  6. M. Khandelwal
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Correspondence to D. J. Armaghani.

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Editorial responsibility: Maryam Shabani.

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Chen, L., Armaghani, D.J., Fakharuab, P. et al. A study on environmental issues of blasting using advanced support vector machine algorithms. Int. J. Environ. Sci. Technol. 19, 6221–6240 (2022). https://doi.org/10.1007/s13762-022-03999-y

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  • DOI: https://doi.org/10.1007/s13762-022-03999-y

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