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Utilizing heuristic strategies for predicting the backbreak occurrences in open-pit mines, Gol Gohar Mine, Iran

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Abstract

Backbreak (BB) is a detrimental outcome of blasting activities in mineral extraction processes within mines. It involves the development of fractures and cracks at considerable distances behind the last row of blast pits, leading to reduced bench safety and increased operational costs. Given the multitude of factors influencing BB, various techniques have been developed to predict and optimize its occurrence. This particular study focused on analyzing 48 blasts in the tailings section of Gol Gohar Mine No. 1 to forecast BB using the whale optimization algorithm (WOA), multiverse optimizer (MVO), sine cosine algorithm (SCA), ant lion optimizer (ALO), and multivariate linear regression (MLR). Comparative analysis of the four BB prediction models revealed that the MVO algorithm yielded the most favorable outcomes, with the train data exhibiting parameter values of 0.9901, 0.2161, 0.1127, 98.8472, and 0.0180 for R2, RMSE, MSE, VAF, and MAPE, respectively, while the test data displayed values of 0.6357, 1.4955, 1.2003, 63.5472, and 0.1951 for the same parameters. In addition, the analysis specifically emphasized the substantial influence of spacing, burden, and GSI as the primary determinants of the backbreak phenomenon. In stark contrast, however, powder factor, delay time, and joint condition are identified as having negligible effects on backbreak.

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References

  • Agrawal H, Mishra A (2019) Modified scaled distance regression analysis approach for prediction of blast-induced ground vibration in multi-hole blasting. J Rock Mech Geotech Eng 11(1):202–207

    Article  Google Scholar 

  • Al-Bakri AY, Sazid M (2021) Application of artificial neural network (ANN) for prediction and optimization of blast-induced impacts. Mining 1(3):315–334

    Article  Google Scholar 

  • Armaghani DJ, Koopialipoor M, Bahri M, Hasanipanah M, Tahir M (2020a) A SVR-GWO technique to minimize flyrock distance resulting from blasting. Bull Eng Geol Env 79(8):4369–4385

    Article  Google Scholar 

  • Armaghani DJ, Mirzaei F, Shariati M, Trung NT, Shariati M, Trnavac D (2020b) Hybrid ANN-based techniques in predicting cohesion of sandy-soil combined with fiber. Geomech Eng 20(3):191–205

    Google Scholar 

  • Bhagade NV, Murthy V (2020) Controlling backbreak and enhancing fragmentation in dragline bench blasting—a geo-engineering approach. Arab J Geosci 13(7):1–20

    Article  Google Scholar 

  • Bhandari S (1997) Engineering rock blasting operations

  • Dai Y, Khandelwal M, Qiu Y, Zhou J, Monjezi M, Yang P (2022) A hybrid metaheuristic approach using random forest and particle swarm optimization to study and evaluate backbreak in open-pit blasting. Neural Comput Appl 34:1–16

    Article  Google Scholar 

  • Eskandar H, Heydari E, Hasanipanah M, Masir MJ, Derakhsh AM (2018) Feasibility of particle swarm optimization and multiple regression for the prediction of an environmental issue of mine blasting. Eng Comput 35:363–376

    Article  Google Scholar 

  • Esmaeili M, Osanloo M, Rashidinejad F, Aghajani Bazzazi A, Taji M (2014) Multiple regression, ANN and ANFIS models for prediction of backbreak in the open pit blasting. Eng Comput 30:549–558

    Article  Google Scholar 

  • Ghanizadeh AR, Delaram A, Fakharian P, Armaghani DJ (2022) Developing predictive models of collapse settlement and coefficient of stress release of sandy-gravel soil via evolutionary polynomial regression. Appl Sci 12(19):9986

    Article  CAS  Google Scholar 

  • Hasanipanah M, Bakhshandeh Amnieh H (2021) Developing a new uncertain rule-based fuzzy approach for evaluating the blast-induced backbreak. Eng Comput 37(3):1879–1893

    Article  Google Scholar 

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

    Article  ADS  Google Scholar 

  • He B, Armaghani DJ, Lai SH (2023) Assessment of tunnel blasting-induced overbreak: a novel metaheuristic-based random forest approach. Tunn Undergr Space Technol 133:104979

    Article  Google Scholar 

  • Indraratna B, Armaghani DJ, Correia AG, Hunt H, Ngo T (2023) Prediction of resilient modulus of ballast under cyclic loading using machine learning techniques. Transport Geotech 38:100895

    Article  Google Scholar 

  • Jayasinghe B, Zhao Z, Chee AGT, Zhou H, Gui Y (2019) Attenuation of rock blasting induced ground vibration in rock-soil interface. J Rock Mech Geotech Eng 11(4):770–778

    Article  Google Scholar 

  • Khoshalan HA, Shakeri J, Najmoddini I, Asadizadeh M (2021) Forecasting copper price by application of robust artificial intelligence techniques. Resour Policy 73:102239

    Article  Google Scholar 

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

    Google Scholar 

  • Kumar S, Mishra A, Choudhary B (2021) Prediction of back break in blasting using random decision trees. Eng Comput 38:1–7

    Google Scholar 

  • Li D, Liu Z, Xiao P, Zhou J, Armaghani DJ (2022a) Intelligent rockburst prediction model with sample category balance using feedforward neural network and Bayesian optimization. Undergr Space 7(5):833–846

    Article  Google Scholar 

  • Li C, Zhou J, Tao M, Du K, Wang S, Armaghani DJ, Mohamad ET (2022b) Developing hybrid ELM-ALO, ELM-LSO and ELM-SOA models for predicting advance rate of TBM. Transport Geotech 36:100819

    Article  Google Scholar 

  • Mirjalili S (2015) The ant lion optimizer. Adv Eng Softw 83:80–98

    Article  Google Scholar 

  • Mirjalili S (2016) SCA: a sine cosine algorithm for solving optimization problems. Knowl-Based Syst 96:120–133

    Article  Google Scholar 

  • Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67

    Article  Google Scholar 

  • Mirjalili S, Mirjalili SM, Hatamlou A (2016) Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Comput Appl 27(2):495–513

    Article  Google Scholar 

  • Momeni E, Armaghani DJ, Hajihassani M, Amin MFM (2015) Prediction of uniaxial compressive strength of rock samples using hybrid particle swarm optimization-based artificial neural networks. Measurement 60:50–63

    Article  ADS  Google Scholar 

  • Monjezi M, Dehghani H, Shakeri J, Mehrdanesh A (2021) Optimization of prediction of flyrock using linear multivariate regression (LMR) and gene expression programming (GEP)—Topal Novin mine Iran. Arab J Geosci 14:1–12

    Article  Google Scholar 

  • Moomivand H, Amini Khoshalan H, Shakeri J, Vandyousefi H (2022) Development of new comprehensive relations to assess rock fragmentation by blasting for different open pit mines using GEP algorithm and MLR procedure. Int J Min Geo-Eng 56(4):401–411

    Google Scholar 

  • Onyelowe KC, Mahesh CB, Srikanth B, Nwa-David C, Obimba-Wogu J, Shakeri J (2021) Support vector machine (SVM) prediction of coefficients of curvature and uniformity of hybrid cement modified unsaturated soil with NQF inclusion. Clean Eng Technol 5:100290

    Article  Google Scholar 

  • Onyelowe KC, Shakeri J, Amini-Khoshalan H, Usungedo TF, Alimoradi-Jazi M (2022) Computational modeling of desiccation properties (CW, LS, and VS) of waste-based activated ash-treated black cotton soil for sustainable subgrade using artificial neural network, gray-wolf, and moth-flame optimization techniques. Adv Mater Sci Eng 2022:1–17

    Article  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. Transport Geotech 31:100652

    Article  Google Scholar 

  • Saghatforoush A, Monjezi M, Shirani Faradonbeh R, Jahed Armaghani D (2016) Combination of neural network and ant colony optimization algorithms for prediction and optimization of flyrock and back-break induced by blasting. Eng Comput 32(2):255–266

    Article  Google Scholar 

  • Saharan MR, Sazid M, Singh TN (2017) Explosive energy utilization enhancement with air-decking and stemming plug, ‘SPARSH.’ Procedia Eng 191:1211–1217

    Article  Google Scholar 

  • Sayadi A, Monjezi M, Talebi N, Khandelwal M (2013) A comparative study on the application of various artificial neural networks to simultaneous prediction of rock fragmentation and backbreak. J Rock Mech Geotech Eng 5(4):318–324

    Article  Google Scholar 

  • Sazid M, Singh TN (2013) Two-dimensional dynamic finite element simulation of rock blasting. Arab J Geosci 6:3703–3708

    Article  Google Scholar 

  • Sazid M, Singh TN (2015) Numerical assessment of spacing–burden ratio to effective utilization of explosive energy. Int J Min Sci Technol 25(2):291–297

    Article  Google Scholar 

  • Shakeri J, Shokri BJ, Dehghani H (2020) Prediction of blast-induced ground vibration using gene expression programming (GEP), artificial neural networks (ANNS), and linear multivariate regression (LMR). Arch Min Sci 65(2):317–335

    Google Scholar 

  • Shakeri J, Amini Khoshalan H, Dehghani H, Bascompta M, Onyelowe K (2022a) Developing new models for flyrock distance assessment in open-pit mines. J Min Environ 13(2):375–389

    Google Scholar 

  • Shakeri J, Asadizadeh M, Babanouri N (2022b) The prediction of dynamic energy behavior of a Brazilian disk containing nonpersistent joints subjected to drop hammer test utilizing heuristic approaches. Neural Comput Appl 34(12):9777–9792

    Article  Google Scholar 

  • Shakeri J, Bascompta M, Alimoradijazi M, Dehghani H (2023) Application of artificial intelligence techniques for predicting the flyrock, Sungun mine Iran. Arab J Geosci 16(8):488

    Article  Google Scholar 

  • Shan F, He X, Armaghani DJ, Zhang P, Sheng D (2022) Success and challenges in predicting TBM penetration rate using recurrent neural networks. Tunn Undergr Space Technol 130:104728

    Article  Google Scholar 

  • Sharma M, Agrawal H, Choudhary B (2021) Multivariate regression and genetic programming for prediction of backbreak in open-pit blasting. Neural Comput Appl 34:1–12

    Google Scholar 

  • Sohrabi P, Jodeiri Shokri B, Dehghani H (2021) Predicting coal price using time series methods and combination of radial basis function (RBF) neural network with time series. Mineral Econ 36:1–10

    Google Scholar 

  • Sohrabi P, Dehghani H, Rafie R (2022) Forecasting of WTI crude oil using combined ANN-Whale optimization algorithm. Energy Sourc Part B 17(1):2083728

    Article  Google Scholar 

  • Yu Q, Monjezi M, Mohammed AS, Dehghani H, Armaghani DJ, Ulrikh DV (2021) Optimized support vector machines combined with evolutionary random forest for prediction of back-break caused by blasting operation. Sustainability 13(22):12797

    Article  Google Scholar 

  • Zhou J, Dai Y, Khandelwal M, Monjezi M, Yu Z, Qiu Y (2021) Performance of hybrid SCA-RF and HHO-RF models for predicting backbreak in open-pit mine blasting operations. Nat Resour Res 30(6):4753–4771

    Article  Google Scholar 

Download references

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Authors and Affiliations

  1. Shahrood University of Technology, Shahrud, Iran

    Parviz Sorabi, Mohammad Ataei & Mohammad Hosein Habibi

  2. KN Toosi University of Technology, Tehran, Iran

    Mohammad Reza Alimoradi Jazi

  3. Hamedan University of Technology, Hamedan, Iran

    Hesam Dehghani & Jamshid Shakeri

Authors
  1. Parviz Sorabi
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  2. Mohammad Ataei
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  3. Mohammad Reza Alimoradi Jazi
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  4. Hesam Dehghani
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  5. Jamshid Shakeri
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  6. Mohammad Hosein Habibi
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Correspondence to Hesam Dehghani.

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Sorabi, P., Ataei, M., Jazi, M.R.A. et al. Utilizing heuristic strategies for predicting the backbreak occurrences in open-pit mines, Gol Gohar Mine, Iran. Soft Comput (2024). https://doi.org/10.1007/s00500-023-09613-8

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