Skip to main content

Advertisement

SpringerLink
Log in

Soft Computing Applications for Optimum Rock Fragmentation: An Advanced Overview

  • State-of-the-Art Review
  • Published:
Geotechnical and Geological Engineering Aims and scope Submit manuscript

Abstract

Rock fragmentation is an important phenomenon in mining engineering as its outcome determines the productivity of the entire mining process. Its imperativeness in mining engineering has attracted the attention of various researchers towards its assessments and various methods have been developed for this purpose. Hence, this study chronicle the methods that had been developed for the fragmentation analysis, identify their strengths and weaknesses. The aim is achieved by first harvesting the articles on rock fragmentation from the Scopus database. The mined data are then subjected to bibliometric analysis using the VoSViewer. Thereafter, detailed information about theoretical background of the rock fragmentation and the methods of analysis are provided. The outcome of the study has revealed that different soft computing (SC) methods are available for the assessment of rock fragmentation aside the traditional methods. The performance of the SC methods are better than many existing empirical methods. The practical application of the SC methods is still very unrealistic as they are largely not in the tractable mathematical form. There are some SC methods that are yet to be explored in rock fragmentation assessment. The study has therefore provided the latest and detailed information on the rock fragmentation by blasting.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

Data Availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

References

  • Adebayo B, Mukoya JGM (2019) Rock properties and machine parameters evaluation at Rössing uranium mine for optimum drill performance. J South Afr Inst Min Metall 119:459–464

    Article  CAS  Google Scholar 

  • Adeel A, Larijani H, Javed A, Ahmadinia A (2015) Impact of learning algorithms on random neural network based optimization for LTE-UL systems. Netw Protoc Algorithms 7:157–178

    Article  Google Scholar 

  • Agyei G, Asssua P (2022) Predicting fragmentation distribution of rock blasting at Eshiem pit of Aliko Resources Limited. Ghana Nigerian J Techn 41(4):651–661

    Article  Google Scholar 

  • Akande JM, Lawal AI (2013) Optimization of blasting parameters using regression models in Ratcon and NSCE granite quarries, Ibadan, Oyo State Nigeria. J Scientific Res (geomaterial) 3:28–37

    Google Scholar 

  • Asl PF, Monjezi M, Hamidi JK, Armaghani DJ (2018) Optimization of flyrock and rock fragmentation in the Tajareh limestone mine using metaheuristics method of firefly algorithm. Eng Comput 34(2):241–251

    Article  Google Scholar 

  • Babaeian M, Ataei M, Sereshki F, Sotoudeh F, Mohammadi S (2019) A new framework for evaluation of rock fragmentation in open pit mines. J Rock Mech Geotechn Eng 11(2):325–336

    Article  Google Scholar 

  • Bahrami A, Monjezi M, Goshtasbi K, Ghazvinian A (2011) Prediction of rock fragmentation due to blasting using artificial neural network. Eng Comput 27(2):177–181

    Article  Google Scholar 

  • Bamford T, Esmaeili K, Schoellig AP (2021) A deep learning approach for rock fragmentation analysis. Inter J Rock Mech Min Sci 145:104839

    Article  Google Scholar 

  • Bendezu M, Romanel C, Roehl D (2017) Finite element analysis of blast-induced fracture propagation in hard rocks. Comput Struct 182:1–13

    Article  Google Scholar 

  • Bergman OR, Riggle JW, Wu FC (1973) Model rock blasting – Effect of explosives properties and other variables on blasting results. Int J Rock Mech Min Sci Geomech Abst 10:586–612

    Article  Google Scholar 

  • Bhandari S (1997) Operations. Engineering Rock Blasting; A.A, Balkema, Rotterdam, The Netherlands

    Google Scholar 

  • Cardu M, Coragliotto D, Oreste P (2019) Analysis of predictor equations for determining the blast-induced vibration in rock blasting. Int J Min Sci Technol 29(6):905–915

    Article  Google Scholar 

  • Carlsson O, Nyberg L (1983) A method for estimation of fragment size distribution with automatic image processing, Proceedings of 1st International. Symposium of Rock Fragmentation by Blasting, Lulea, Sweden, pp. 333–345.

  • Chakraborty AK, Raina AK, Ramulu M, Choudhury PB, Haldar A, Sahu P, Bandopadhyay C (2004) Parametric study to develop guidelines for blast fragmentation improvement in jointed and massive formations. Eng Geol 73:105–116

    Article  Google Scholar 

  • Changyou L, Jingxuan Y, Bin Y (2017) Rock-breaking mechanism and experimental analysis of confined blasting of borehole surrounding rock. Int J Min Sci Technol 2017(27):795–801

    Article  Google Scholar 

  • Cho SH, Kaneko K (2004) Influence of the applied pressure waveform on the dynamic fracture processes in rock. Int J Rock Mech Min Sci 41:771–784

    Article  Google Scholar 

  • Cho S, Nakamura Y, Mohanty B, Yang H, Kaneko K (2008) Numerical study of fracture plane control in laboratory-scale blasting. Eng Fract Mech 75:3966–3984

    Article  Google Scholar 

  • Chung SH, Katsabanis PD (2000) Fragmentation prediction using improved engineering formulae. Fragblast 4(3–4):198–207

    Article  Google Scholar 

  • Cunningham CVB (1983) The KuzRam Model for Prediction of Fragmentation from Blasting. In: Proceedings of 1st International Symposium on Rock Fragmentation by Blasting, Lulea, Sweden, pp 440–453.

  • Cunningham CVB (1987) Fragmentation estimations and KuzRam model – Four Years On. In: Proceedings of 2nd Int. Symposium on Rock Fragmentation by Blasting, Keystone, Colorado, pp 475–487.

  • Dahlhielm S (1996) Industrial applications of image analysis - The IPACS System. Measurement of Blast Fragmentation: Proceedings of the Fragblast-5 Workshop, Montreal, Quebec, Canada, 23–24 August 1996. Franklin, J.A. and and Katsabanis, P.D. (eds). Balkema, Rotterdam. pp 67–71.

  • Dare-Bryan P, Mansfield S, Schoeman J (2012) Blast optimisation through computer modelling of fragmentation, heave and damage. Rock Fragmentation by Blasting. CRC Press, Boca Raton, FL, pp 95–104

    Google Scholar 

  • Da Gama CD (1970) Laboratory studies of comminution in rock blasting. PhD Thesis, University of Minnesota

  • Das A, Sinha S, Ganguly S (2019) Development of a blast-induced vibration prediction model using an artificial neural network. J South Afr Inst Min Metall 119:187–200

    Article  Google Scholar 

  • Demuth H, Beale M (1994) Neural network toolbox. The Math Works, Inc.

  • Dhekne P, Pradhan M, Jade RK (2015) Assessment of the effect of blast hole diameter on the number of oversize boulders using ANN Model. J Instit Engineers India Series D 97(1):21–31

    Article  ADS  Google Scholar 

  • Dhekne PY, Pradhan M, Jade RK, Mishra R (2017) Boulder prediction in rock blasting using artificial neural network. ARPN J Eng Appl Sci 12(1):47–61

    Google Scholar 

  • Dimitraki L, Christaras B, Marinos V, Vlahavas I, Arampelos N (2019) Predicting the average size of blasted rocks in aggregate quarries using artificial neural networks. Bull Eng Geol Environ 78(4):2717–2729

    Article  Google Scholar 

  • Dowding CH (1985) Blast vibration monitoring and control. Prentice-Hall, Englewoods Cliffs, NJ

    Google Scholar 

  • Duvall WI, Petkof B (1959) Spherical propagation of explosion of generated strain pulses in rocks, USBM, RI-5483 pp 21–2.

  • Ebrahimi E, Monjezi M, Khalesi MR, Armaghani DJ (2016) Prediction and optimization of back-break and rock fragmentation using an artificial neural network and a bee colony algorithm. Bull Eng Geol Environ 75(1):27–36

    Article  Google Scholar 

  • Elahi AT, Hosseini M (2017) Analysis of blasted rocks fragmentation using digital image processing (case study: limestone quarry of Abyek Cement Company). Geo-Eng 8:16

    Article  Google Scholar 

  • Enayatollahi I, Aghajani Bazzazi A, Asadi A (2014) Comparison between neural networks and multiple regression analysis to predict rock fragmentation in open-pit mines. Rock Mech Rock Eng 47:799–807

  • Esmaeili M, Salimi A, Drebenstedt C, Abbaszadeh M, Bazzazi AA (2015) Application of PCA, SVR, and ANFIS for modeling of rock fragmentation. Arab J Geosci 8(9):6881–6893

    Article  Google Scholar 

  • Fourney WL (1993) 2—Mechanisms of Rock Fragmentation by blasting. In Excavation, Support and Monitoring Hudson, J.A., Ed. Pergamon, Oxford, UK, pp 39–69.

  • Franklin JA, Maerz NH (2018) Empirical design and rock mass characterization. Routledge, Measurement of Blast Fragmentation

    Book  Google Scholar 

  • Gao W, Karbasi M, Hasanipanah M, Zhang X, Guo J (2018) Developing GPR model for forecasting the rock fragmentation in surface mines. Eng Comput 34(2):339–345

    Article  Google Scholar 

  • Gheibie S, Aghababaei H, Hoseinie SH, Pourrahimian Y (2009) Modified Kuz—Ram fragmentation model and its use at the sungun copper mine. Int J Rock Mech Min Sci 46(6):967–973

    Article  Google Scholar 

  • Grant JR, Dutton AJ (1983) Development of a fragmentation monitoring system for evaluating open slope blast performance at Mount Isa Mines. Proc 1st Int Symp Rock Frag Blasting, Lulea, Sweden pp. 637–652.

  • Gui Y, Zhao Z, Zhou H, Goh A, Jayasinghe L (2017) Numerical simulation of rock blasting induced free field vibration. Procedia Eng 191:451–457

    Article  Google Scholar 

  • Hagan MT, Menhaj M (1999) Training feed-forward networks with the Marquardt algorithm. IEEE Trans Neural Netw 5(6):989–993. https://doi.org/10.1109/72.329697

    Article  Google Scholar 

  • Havermann, T. and Vogt, W. 1996. TUCIPS - A system for the estimation of fragmentation after production blasts. Measurement of Blast Fragmentation: Proceedings of the Fragblast-5 Workshop on Measurement of Blast Fragmentation, Montreal, Quebec, Canada, 23–24 August 1996. Franklin, J.A. and Katsabanis, P.D. (eds). Balkema, Rotterdam. pp 59–65.

  • Hunter GC, McDermott C, Miles NJ, Singh A, Scoble MJ (1990) A review of image analysis techniques for measuring blast fragmentation. Min Sci Tech 11:19–36

    Article  Google Scholar 

  • Haykin S (1994) Neural Networks: A Comprehensive Foundation. MacMillan College Publishing Co., New York

    Google Scholar 

  • Huang Y (2009) Advances in artificial neural networks—methodological development and application. Algorithms 2:973–1007

    Article  MathSciNet  Google Scholar 

  • Huang GB, Zhu QY, Siew CK (2006) Extreme learning machine: theory and applications. Neurocomput 70:489–501

    Article  Google Scholar 

  • Hüdaverdi T, Akyıldız Ö (2020) Investigation of blast fragmentation models in a sandstone quarry. Madencilik 59(3):145–156

    Article  Google Scholar 

  • Idowu KA, Olaleye BM, Saliu MA (2021a) Analysis of blasted rocks fragmentation using digital image processing (Case study: Limestone quarry of Obajana Cement Company). Min Mineral Dep 15(4):34–42

    Article  Google Scholar 

  • Idowu KA, Olaleye BM, Saliu MA (2021b) Application of split desktop image analysis and Kuz-Ram empirical model for evaluation of blast fragmentation efficiency in a typical granite quarry. Ghana Min J 21(1):45–52

    Article  Google Scholar 

  • Illias HA, Chai XR, Abu Bakar AH, Mokhlis H (2015) Transformer incipient fault prediction using combined artificial neural network and various particle swarm optimisation techniques. PLoS ONE 10(6):e0129363

    Article  PubMed  PubMed Central  Google Scholar 

  • Jain AK, Mao J, Mohiuddin KM (1996) Artificial neural networks-a tutorial. J IEEE Comp 3:31–44

    Article  Google Scholar 

  • Jang RJ (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE TransSys, Man Cybernetics 23:665–685

    Article  Google Scholar 

  • Jang JSR, Sun CT, Mizutani E (1997) Neuro-fuzzy and soft computing, a computational approach to learning and machine intelligence, 1st edn. Prentice Hall, New Jersey

    Google Scholar 

  • Jimeno CL, Jimeno EL, Carcedo FJA, de Ramiro YV (1995) Drilling and Blasting of Rocks; CRC Press: Boca Raton. FL, USA

    Google Scholar 

  • Kabwe E (2018) Velocity of detonation measurement and fragmentation analysis to evaluate blasting efficacy. J Rock Mech Geotech Eng 10:523–533

    Article  Google Scholar 

  • Kansake BA, Temeng VA, Afum BO (2016) Comparative analysis of rock fragmentation models – A case study. 4th UMaT Biennial IntMin Mineral Conf pp. MP pp 1 – 11.

  • Khandelwal M, Singh T (2007) Evaluation of blast-induced ground vibration predictors. Soil Dyn Earthq Eng 27:116–125

    Article  Google Scholar 

  • Kou S, Rustan A (1993) Computerized design and result prediction of bench blasting. In: Proceedings of the 4th International Symposium on Rock Fragmentation by Blasting, Vienna, Austria, pp 263–271.

  • Kulatilake PHSW, Wu Q, Hudaverdi T, Kuzu C (2010) Mean particle size pre-diction in rock blast fragmentation using neural networks. Eng Geol 114(3–4):298–311

    Article  Google Scholar 

  • Kulatilake PHSW, Hudaverdi T, Wu Q (2012) New prediction models for mean particle size in rock blast fragmentation. Geotech Geol Eng 30(3):665–684

    Article  Google Scholar 

  • Kuznetsov VM (1973) Mean diameter of fragments formed by blasting rock. Soviet Min Sci 9(2):144–148

    Article  Google Scholar 

  • Larsson B (1974) Report on Blasting of High and Low Benches – Fragmentation From Production Blasts. In: Proceedings of the Swedish Rock Construction Committee Discussion Meeting BK 74, Stockholm,pp 247–273.

  • Lawal AI (2020) An artificial neural network-based mathematical model for the prediction of blast-induced ground vibration in granite quarries in Ibadan, Oyo State, Nigeria. Scientific Afr 8:1–10

    Google Scholar 

  • Lawal AI (2021) A new modification to the Kuz-Ram model using the fragment size predicted by image analysis. Int J Rock Mech Min Sci 138:1–6

    Article  Google Scholar 

  • Lawal AI, Idris MA (2019) An artificial neural network-based mathematical model for the prediction of blast-induced ground vibrations. Int J Environ Std 77(2):318–334

    Article  Google Scholar 

  • Lawal AI, Kwon S (2020) Application of artificial intelligence in rock mechanics: an overview. J Rock Mech Geotech Eng 13:248–266

    Article  Google Scholar 

  • Lawal AI, Kwon S (2023) Development of mathematically motivated hybrid soft computing models for improved predictions of ultimate bearing capacity of shallow foundations. J Rock Mech Geotech Eng 15(3):747–759

    Article  Google Scholar 

  • Lawal AI, Aladejare EA, Onifade M, Bada S, Idris MA (2020) Predictions of elemental composition of coal and biomass from their proximate analyses using ANFIS, ANN and MLR. Int J Coal Sci Techn 8(1):124–140

    Article  Google Scholar 

  • Lawal AI, Kwon S, Kim GY (2021a) Prediction of an environmental impact of tunnel blasting using artificial neural network, particle swarm and Dragonfly optimized artificial neural networks. Applied Acou 181:1–15

    Google Scholar 

  • Lawal AI, Kwon S, Kim GY (2021b) Prediction of the blast-induced ground vibration in tunnel blasting using ANN, moth-flame optimized ANN, and gene expression programming. Acta Geophys 69:161–174

    Article  ADS  Google Scholar 

  • Lawal AI, Kwon S, Hammed OS, Idris MA (2021c) Blast-induced ground vibration prediction in granite quarries: An application of Gene expression programming, ANFIS, and Sine Cosine algorithm optimized ANN. Int J Min Sci Techn 31:265–277

    Article  Google Scholar 

  • Lawal AI, Olajuyi SI, Kwon S, Aladejare AE, Edo TM (2021d) Prediction of blast-induced ground vibration using GPR and blast-design parameters optimization based on novel grey-wolf optimization algorithm. Acta Geophys 69:1313–1324

    Article  ADS  Google Scholar 

  • Lawal AI, Olajuyi SI, Kwon S, Onifade M (2021e) A comparative application of Buckingham π (Pi) theorem, white-box ANN, gene expression programming, and multilinear regression approaches for blast-induced ground vibration prediction. Arab J Geosci 14(12):1–15

    Article  Google Scholar 

  • Lawal AI, Aladejare AE, Onifade M, Kwon S (2022a) Development of soft computing based mathematical models for predicting mean fragment size using response surface method parameters. Arab J Geosci 5(2):1–16

    Google Scholar 

  • Lawal AI, Ojo OJ, Kim M, Kwon S (2022b) Determination of blast-induced flyrock using a drone technology: a bibliometric overview with practical soft computing implementation. Arab J Geosci 15(19):1–18

    Article  Google Scholar 

  • Lilly PA (1986) An Empirical Method of Assessing Rock Mass Blastability. In: Proceedings of the Large Open Pit Mining Conference, Victoria, Australia, pp 89–92.

  • Maerz NH, Palangio TC, Franklin JA (1996) The Wipfrag image based granulometry system. Measurement of Blast Fragmentation: Proceedings of the Fragblast-5 Workshop on Measurement of Blast Fragmentation, Montreal, Quebec, Canada, 23–24 August 1996. Franklin, J.A. and Katsabanis P.D. (eds). Balkema, Rotterdam. pp. 91–98.

  • Majid G, Nematollah A, Dindarloo SR, Hamed S (2016) Prediction of blast boulders in open pit mines via multiple regression and artificial neural networks. Int J Min Sci Techn 26(2):183–186

    Article  Google Scholar 

  • Marto A, Hajihassani M, Armaghani DJ, Mohamad ET, Makhtar AM (2014) A novel approach for blast-induced flyrock prediction based on imperialist competitive algorithm and artificial neural network. The Scientific W J. https://doi.org/10.1155/2014/643715

    Article  Google Scholar 

  • Maulenkamp F, Grima MA (1999) Application of neural networks for the prediction of the unconfined compressive strength (UCS) from Equotip hardness. Int J Rock Mech Min Sci 36(1):29–39

    Article  Google Scholar 

  • McCulloch Warren S, Pitts W (1943) A logical calculus of the ideas immanent in nervous activity. Bull Math Biophys 5:115–133

    Article  MathSciNet  Google Scholar 

  • Mohamad ET, Yi CS, Murlidhar BR, Saad R (2018) Effect of geological structure on flyrock prediction in construction blasting. Geotech Geol Eng 36:2217–2235

    Article  Google Scholar 

  • Mohammadi H, Barati B (2018) Development of a rock fragmentation model for using in tunnel blasts. Geotech Geol Eng 36:2379–2390

    Article  Google Scholar 

  • Mojtahedi SFF, Ebtehaj I, Hasanipanah M, Bonakdari H, Amnieh HB (2019) Proposing a novel hybrid intelligent model for the simulation of particle size distribution resulting from blasting. Eng Comput 35(1):47–56

    Article  Google Scholar 

  • Monjezi M, Mohamadi HA, Barati B, Khandelwal M (2012) Application of soft computing in predicting rock fragmentation to reduce environmental blasting side effects. Arab J Geosci 7:505–511

    Article  Google Scholar 

  • Monjezi M, Mohamadi HA, Barati B, Khandelwal M (2014) Application of soft computing in predicting rock fragmentation to reduce environmental blasting side effects. Arab J Geosci 7(2):505–511

    Article  Google Scholar 

  • Mutinda EK, Alunda BO, Maina DK, Kasomo RM (2021) Prediction of rock fragmentation using the Kuznetsov-Cunningham-Ouchterlony model. J South Afr Inst Min Metall 121(3):107–112

    Article  Google Scholar 

  • Nefis M, Talhi K (2016) A model study to measure fragmentation by blasting. Min Sci 23:91–104

    Google Scholar 

  • Nguyen H, Bui XN, Bui HB, Mai NL (2018) A comparative study of artificial neural networks in predicting blast-induced air-blast overpressure at Deo Nai open-pit coal mine. Vietnam Neural Comput Appl 32:3939–3955

    Article  Google Scholar 

  • Nguyen H, Buil X-N, Tran Q-H, Le T-Q, Do N-H, Hoa LTH (2019) Evaluating and predicting blast-induced ground vibration in open-cast mine using ANN: a case study in Vietnam. SN Appl Sci 1:125

    Article  Google Scholar 

  • Ouchterlony F (2005) The Swebrec© function: linking fragmentation by blasting and crushing. Min Tech - Trans Institutions of Min Metall: Section A 114(1):29–44

    Google Scholar 

  • Ouchterlony F, Sanchidrián JA (2019) A review of development of better prediction equations for blast fragmentation. J Rock Mech Geotech Eng 11(5):1094–1109

    Article  Google Scholar 

  • Paswan RK, Roy MP, Shankar R, Singh PK (2021) Blast vibration and fragmentation control at heavily jointed limestone mine. Geotech Geol Eng 39:3469–3485

    Article  Google Scholar 

  • Rafiei-Sardooi E, Mohseni-Saravi M, Barkholi S, Azareh A, Choubin B, Jafari-Shalamzar M (2018) Drought modelling: A comparative study between time series and neuro-fuzzy approaches. Arab J Geosci 11:487

    Article  Google Scholar 

  • Raina AK, Choudhary PB, Ramulu M, Chakraborty AK, Dudhankar AS, Udpikar V, Ghatpande N, Misra DD (2002) Fragalyst-An indigenous digital image analysis system for fragment size measurement in mines. J Geological Soci India 59:561–569

    Google Scholar 

  • Raina AK, Murthy VMSR, Soni AK (2014) Flyrock in bench blasting: a comprehensive review. Bull Int Assoc Eng Geol 73:1199–1209

    Article  Google Scholar 

  • Rezaei M, Monjezi M, Varjani AY (2011) Development of a fuzzy model to predict flyrock in surface mining. Saf Sci 49(2):298–305

    Article  Google Scholar 

  • Rosin P, Rammler E (1933) The Laws Governing the Fineness of Powdered Coal. J Institute of Fuel 7:29–36

    CAS  Google Scholar 

  • Rustan PA (1998) Automatic image processing and analysis of rock fragmentation— comparison of systems and new guidelines for testing the systems. Fragblast 2(1):15–23

    Article  Google Scholar 

  • Sanchidrián JA, Segarra P, López LM (2007) Energy components in rock blasting. Int J Rock Mech Min Sci 44:130–147

    Article  Google Scholar 

  • Sanchidrián JA, Segarra P, Ouchterlony F, Lopez LM (2009) The accuracy of fragment size measurement by image analysis in combination with some distribution functions. J Rock Mech Rock Eng 42(1):95–116

    Article  ADS  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 

  • Sazli MH (2006) A brief review of feed-forward neural networks. Commun. Fac Sci Univ Ank Series A2-A3 50(1):11–17

    Google Scholar 

  • Schleifer J, Tessier B (1996) Fragscan, a tool to measure fragmentation of blasted rock. Measurement of Blast Fragmentation: Proceedings of the Fragblast-5 Workshop on Measurement of Blast Fragmentation, Montreal, Quebec, Canada, 23–24 August 1996. Franklin, J.A. and Katsabanis, P.D. (eds). Balkema, Rotterdam. pp. 73–78.

  • Scott A, Cocker A, Djordjevic N, Higgins M, La Rosa D, Sarma KS, Wedmaier R (1996) Open Pit Blast Design Analysis and Optimization. Julius Kruttsichnitt Mineral Research Center, Australia

    Google Scholar 

  • Shams S, Monjezi M, Majd VJ, Armaghani DJ (2015) Application of fuzzy inference system for prediction of rock fragmentation induced by blasting. Arab J Geosci 28(12):10819–10832

    Article  Google Scholar 

  • Shang Y, Nguyen H, Bui X-N, Tran Q-H, Moayedi H (2019) A novel artificial intelligence approach to predict blast-induced ground vibration in open-pit mines based on the firefly algorithm and artificial neural network. Nat Resour Res 29:723–737

    Article  Google Scholar 

  • Sharma PA (2012) Rock breakage and blast design considerations in openpit. https://miningandblasting.wordpress.com/tag/fragmentation/ (Retrieved January 2023).

  • Shehu SA, Yusuf KO, Hashim MHM (2020) Comparative study of WipFrag image analysis and Kuz-Ram empirical model in granite aggregate quarry and their application for blast fragmentation rating. J Geomech Geoeng 17(1):197–205

    Article  Google Scholar 

  • Siddiqui F, Shah S, Behan M (2009) Measurement of size distribution of blasted rock using digital image processing. J King Abdulaziz Univ Eng Sci 20(2):81–93. https://doi.org/10.4197/Eng.20-2.4

    Article  Google Scholar 

  • Tessier B (2008) FragScan, Prise dimages et échantillonnage. Unpublished mem-oir, Paris School of Mines, Paris, France, Principes généraux

    Google Scholar 

  • Tosun A, Konak G, Toprak T, Karakus D, Onur AH (2014) Development of the Kuz-Ram model to blasting in a limestone quarry. Arch Min Sci 59(2):477–488

    Google Scholar 

  • Wen L, Luo ZQ, Yang SJ, Qin YG, Wang W (2019) Correlation of geo-mechanics parameters with uniaxial compressive strength and P-wave velocity on dolomitic limestone using a statistical method. Geotech Geol Eng 37:1079–1094

    Article  Google Scholar 

  • Xie C, Nguyen H, Bui X-N, Choi Y, Zhou J, Nguyen-Trang T (2021) Predicting rock size distribution in mine blasting using various novel soft computing models based on meta-heuristics and machine learning algorithms. Geosci Frontiers 12:101108

    Article  Google Scholar 

  • Zhou J, Li C, Arslan CA, Hasanipanah M, Amnieh BH (2021) Performance evaluation of hybrid FFA-ANFIS and GA-ANFIS models to predict particle size distribution of a muck-pile after blasting. Eng Comput 37(1):265–274

    Article  Google Scholar 

Download references

Funding

This work was supported by National Research Foundation of Nigeria (NRF) with code (TETF/DR&D-CE/NRF 2020).

Author information

Authors and Affiliations

  1. Department of Mining Engineering, Federal University of Technology, Akure, Nigeria

    A. I. Lawal, B. Adebayo, T. B. Afeni, I. A. Okewale, E. O. Ajaka & V. A. Akinbinu

  2. Department of Applied Geophysics, Federal University of Technology, Akure, Nigeria

    J. O. Amigun

  3. Department of Computer Engineering, Federal University of Technology, Akure, Nigeria

    W. O. Apena

Authors
  1. A. I. Lawal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. B. Adebayo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. T. B. Afeni
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. I. A. Okewale
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. E. O. Ajaka
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. J. O. Amigun
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. V. A. Akinbinu
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. W. O. Apena
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Adebayo and Lawal. The first draft of the manuscript was written by Lawal. All authors read and approved the final manuscript.

Corresponding author

Correspondence to A. I. Lawal.

Ethics declarations

Conflict of interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lawal, A.I., Adebayo, B., Afeni, T.B. et al. Soft Computing Applications for Optimum Rock Fragmentation: An Advanced Overview. Geotech Geol Eng 42, 859–880 (2024). https://doi.org/10.1007/s10706-023-02594-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10706-023-02594-3

Keywords

Search

Navigation