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A new auto-tuning model for predicting the rock fragmentation: a cat swarm optimization algorithm

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Abstract

The main focus of the present work is to offer an auto-tuning model, called cat swarm optimization (CSO), to predict rock fragmentation. This population-based method has a stochastic formation involving exploration and exploitation phases. CSO is a robust and powerful meta-heuristic algorithm inspired by the behaviors of cats; it is composed of two search modes: seeking and tracing, which can be joined by mixture ratio parameter. CSO is applied to large-scale optimization problems like rock fragmentation to have good forecasting parameters in D80 formulas (D80 is a common descriptor that evaluates rock fragmentation). To evaluate the efficiency of the proposed CSO model, its obtained results were compared to those of the particle swarm optimization (PSO) algorithm. In the modeling, two forms of CSO and PSO models, including power and linear forms, were developed. The comparative results showed that CSO models outperformed the rival in terms of the task defined. The precision of the proposed models was computed using statistical evaluation criteria. Comparison results concluded that CSO-power model with the root mean square error (RMSE) of 0.847 was more computationally efficient with better predictive ability compared to CSO-linear, PSO-linear and PSO-power models with the RMSE of 1.314, 1.545 and 2.307, respectively. Furthermore, the sensitivity analysis revealed the effect of the stemming parameter upon D80 in comparison with other input parameters.

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References

  1. 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

    Google Scholar 

  2. Shams S, Monjezi M, Johari Majd V, Jahed Armaghani D (2015) Application of fuzzy inference system for prediction of rock fragmentation induced by blasting. Arab J Geosci 8:10819–10832

    Google Scholar 

  3. Roy PP, Dhar BB (1996) Fragmentation analyzing scale-a new tool for breakage assessment. In: Proceedings 5th international symposium on rock fragmentation by blasting Montreal; Canada, pp 448

  4. Mishnaevsky JR, Schmauder S (1996) Analysis of rock fragmentation with the use of the theory of fuzzy sets. In: Barla (ed) Proceedings of the Eurock, vol 96, pp 735–740

  5. Marto A, Hajihassani M, Armaghani DJ, Tonnizam Mohamad E, Makhtar AM (2014) A novel approach for blast induced flyrock prediction based on imperialist competitive algorithm and artificial neural network. Sci World J 5:643715

    Google Scholar 

  6. Sadowski L, Nikoo M (2014) Corrosion current density prediction in reinforced concrete by imperialist competitive algorithm. Neural Comput Appl 25(7–8):1627–1638

    Google Scholar 

  7. Jahed Armaghani D, Mohamad ET, Hajihassani M, Abad SANK, Marto A, Moghaddam MR (2015) Evaluation and prediction of flyrock resulting from blasting operations using empirical and computational methods. Eng Comput 32(1):109–121

    Google Scholar 

  8. Hasanipanah M, Armaghani DJ, Amnieh HB, Majid MZA, Tahir MMD (2017) Application of PSO to develop a powerful equation for prediction of flyrock due to blasting. Neural Comput Appl 28(1):1043–1050

    Google Scholar 

  9. Hasanipanah M, Bakhshandeh Amnieh H, Arab H, Zamzam MS (2018) Feasibility of PSO–ANFIS model to estimate rock fragmentation produced by mine blasting. Neural Comput Appl 30(4):1015–1024

    Google Scholar 

  10. Asteris PG, Mokos VG (2020) Concrete compressive strength using artificial neural networks. Neural Comput Appl 32:1807–11826

    Google Scholar 

  11. Sun Y et al (2019) Determination of Young’s modulus of jet grouted coalcretes using an intelligent model. Eng Geol 252:43–53

    Google Scholar 

  12. Sun Y, Li G, Zhang J, Qian D (2019) Prediction of the strength of rubberized concrete by an evolved random forest model. Adv Civ Eng. https://doi.org/10.1155/2019/5198583

    Article  Google Scholar 

  13. Hajihassani M, Abdullah SS, Asteris PG, Armaghani DJ (2019) A gene expression programming model for predicting tunnel convergence. Appl Sci 9:4650

    Google Scholar 

  14. Nikafshan Rad H, Hasanipanah M, Rezaei M, Eghlim AL (2019) Developing a least squares support vector machine for estimating the blast-induced flyrock. Eng Comput 34(4):709–717

    Google Scholar 

  15. Sun J, Zhang J, Gu Y, Huang Y, Sun Y, Ma G (2019) Prediction of permeability and unconfined compressive strength of pervious concrete using evolved support vector regression. Constr Build Mater 207:440–449

    Google Scholar 

  16. Zhou J, Li X, Mitri HS (2015a) Evaluation method of rockburst: state-of-the-art literature review. Tunn Undergr Sp Technol 81:632–659

    Google Scholar 

  17. Qi CC, Chen Q, Sonny SK (2020) Integrated and intelligent design framework for cemented paste backfill: a combination of robust machine learning modelling and multi-objective optimization. Miner Eng 155:106422

    Google Scholar 

  18. Zhou J, Asteris PG, Armaghani DJ, Pham BT (2020) Prediction of ground vibration induced by blasting operations through the use of the Bayesian Network and random forest models. Soil Dyn Earthq Eng 139:106390. https://doi.org/10.1016/j.soildyn.2020.106390

    Article  Google Scholar 

  19. Qi CC (2020) Big data management in the mining industry. Int J Miner Metall Mat. https://doi.org/10.1007/s12613-019-1937-z

    Article  Google Scholar 

  20. 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). Int J Min Reclam Environ. https://doi.org/10.1080/17480930.2020.1734151

    Article  Google Scholar 

  21. Ding X, Hasanipanah M, Rad HN, Zhou W (2020) Predicting the blast-induced vibration velocity using a bagged support vector regression optimized with firefly algorithm. Eng Comput. https://doi.org/10.1007/s00366-020-00937-9

    Article  Google Scholar 

  22. Hasanipanah M, Keshtegar B, Thai DK, Troung NT (2020) An ANN-adaptive dynamical harmony search algorithm to approximate the flyrock resulting from blasting. Eng Comput. https://doi.org/10.1007/s00366-020-01105-9

    Article  Google Scholar 

  23. Qi CC, Chen Q, Dong X, Zhang Q, Yaseen ZM (2020) Pressure drops of fresh cemented paste backfills through coupled test loop experiments and machine learning techniques. Powder Technol 361:748–758

    Google Scholar 

  24. Sun Y, Li G, Zhang J, Sun J, Xu J (2020) Development of an ensemble intelligent model for assessing the strength of cemented paste backfill. Adv Civ Eng. https://doi.org/10.1155/2020/1643529

    Article  Google Scholar 

  25. Sun Y, Li G, Zhang J (2020) Developing hybrid machine learning models for estimating the unconfined compressive strength of jet grouting composite: a comparative study. Appl Sci 10(5):1612

    Google Scholar 

  26. Sun Y, Li G, Zhang N, Chang Q, Xu J, Zhang J (2020) Development of ensemble learning models to evaluate the strength of coal-grout materials. Int J Min Sci Technol. https://doi.org/10.1016/j.ijmst.2020.09.002

    Article  Google Scholar 

  27. Hasanipanah M, Zhang W, Armaghani DJ, Rad HN (2020) The potential application of a new intelligent based approach in predicting the tensile strength of rock. IEEE Access 8:57148–57157

    Google Scholar 

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

    Google Scholar 

  29. Sayevand K, Arab H, Bagheri Golzar S (2018) Development of imperialist competitive algorithm in predicting the particle size distribution after mine blasting. Eng Comput 34(2):329–338

    Google Scholar 

  30. Shi XZ, Zhou J, Wu B, Huang D, Wei W (2012) Support vector machines approach to mean particle size of rock fragmentation due to bench blasting prediction. Trans Nonferrous Met Soc China 22:432–441

    Google Scholar 

  31. Monjezi M, Bahrami A, Yazdian Varjani A (2010) Simultaneous prediction of fragmentation and flyrock in blasting operation using artificial neural networks. Int J Rock Mech Min Sci 47(3):476–480

    Google Scholar 

  32. Monjezi M, Rezaei M, Yazdian Varjani A (2009) Prediction of rock fragmentation due to blasting in Gol-E-Gohar iron mine using fuzzy logic. Int J Rock Mech Min Sci 46:1273–1280

    Google Scholar 

  33. Esmaeili M, Salimi A, Drebenstedt C, Abbaszadeh M, AghajaniBazzazi A (2014) Application of PCA, SVR, and ANFIS for modeling of rock fragmentation. Arab J Geosci 8(9):6881–6893

    Google Scholar 

  34. Salimi AR, Esmaeili M, Drebenstedt C, Dehghani MH (2012) A neurofuzzy approach for prediction of rock fragmentation in open pit mines. In: Proc. 21th int. symp. on mine planning & equipment selection (MPES), New Delhi, India, pp 656–666

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

    Google Scholar 

  36. 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

    Google Scholar 

  37. Zhou J, Li C, Arslan CA, Hasanipanah M, Amnieh HB (2019) Performance evaluation of hybrid FFA-ANFIS and GA-ANFIS models to predict particle size distribution of a muck-pile after blasting. Eng Comput. https://doi.org/10.1007/s00366-019-00822-0

    Article  Google Scholar 

  38. Sayevand K, Arab H (2019) A fresh view on particle swarm optimization to develop a precise model for predicting rock fragmentation. Eng Comput 36(2):533–550

    Google Scholar 

  39. Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: Proceedings of IEEE international conference on neural networks, Piscataway: 1942–1948

  40. Hajihassani M, Jahed Armaghani D, Sohaei H, Tonnizam Mohamad E, 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

    Google Scholar 

  41. Hasanipanah M, Naderi R, Kashir J, Noorani SA, Aaq Qaleh AZ (2017) Prediction of blast produced ground vibration using particle swarm optimization. Eng Comput 33(2):173–179

    Google Scholar 

  42. Qi CC, Fourie A, Chen QS (2018) Neural network and particle swarm optimization for predicting the unconfined compressive strength of cemented paste backfill. Constr Build Mater 159:473–478

    Google Scholar 

  43. Hajihassani M, Armaghani DJ, Kalatehjari R (2018) Applications of particle swarm optimization in geotechnical engineering: a comprehensive review. Geotech Geol Eng 36(2):705–722

    Google Scholar 

  44. Yang H, Hasanipanah M, Tahir MM, Bui DT (2019) Intelligent prediction of blasting-induced ground vibration using ANFIS optimized by GA and PSO. Nat Resour Res. https://doi.org/10.1007/s11053-019-09515-3

    Article  Google Scholar 

  45. Yang H, Nikafshan Rad H, Hasanipanah M, Amnieh HB, Nekouie A (2019) Prediction of vibration velocity generated in mine blasting using support vector regression improved by optimization algorithms. Nat Resour Res. https://doi.org/10.1007/s11053-019-09597-z

    Article  Google Scholar 

  46. Sarir P, Chen J, Asteris PG, Armaghani DJ, Tahir MM (2019) Developing GEP tree-based, neuro-swarm, and whale optimization models for evaluation of bearing capacity of concrete-filled steel tube columns. Eng Comput. https://doi.org/10.1007/s00366-019-00808-y

    Article  Google Scholar 

  47. Gilani SO, Sattarvand J, Hajihassani M, Abdullah SS (2020) A stochastic particle swarm based model for long term production planning of open pit mines considering the geological uncertainty. Resour Policy 68:101738

    Google Scholar 

  48. Hajihassani M, Kalatehjari R, Marto A et al (2020) 3D prediction of tunneling-induced ground movements based on a hybrid ANN and empirical methods. Eng Comput 36:251–269

    Google Scholar 

  49. Hasanipanah M, Bakhshandeh Amnieh H (2020) A fuzzy rule based approach to address uncertainty in risk assessment and prediction of blast-induced flyrock in a quarry. Nat Resour Res. https://doi.org/10.1007/s11053-020-09616-4

    Article  Google Scholar 

  50. Chu SC, Tsai PW, Pan JS (2006) Cat swarm optimization. In: Proceedings of the 9th Pacific Rim international conference on artificial intelligence (PRICAI), pp 854–858

  51. Dorigo M, Gambardella LM (1997) Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans Evolut Comput 26(1):53–66

    Google Scholar 

  52. Chu SC, Tsai PW (2007) Computational intelligence based on the behavior of cats. Int J Innov Comput Inform Control 3:163–173

    Google Scholar 

  53. Bonabeau M (1999) Swarm intelligence: from natural to artificial systems. Oxford University Press, Oxford

    MATH  Google Scholar 

  54. Fister I, Yang I, Fister J, Fister D (2013) A brief review of nature-inspired algorithms for optimization. Elektrotehniški Vestnik 80:1–7

    MATH  Google Scholar 

  55. Panda G, Pradhan PM, Majhi B (2011) IIR system identification using cat swarm optimization. Expert Syst Appl 38:12671–12683

    Google Scholar 

  56. Wang ZH, Chang CC, Li MC (2012) Optimizing least-significant-bit substitution using cat swarm optimization strategy. Inf Sci 192:98–108

    Google Scholar 

  57. Pradhan PM, Panda G (2012) Solving multi objective problems using cat swarm optimization. Expert Syst Appl 39:2956–2964

    Google Scholar 

  58. Tsai PW, Pan JS, Chen SM, Liao BY (2012) Enhanced parallel cat swarm optimization based on the Taguchi method. Expert Syst Appl 39:6309–6319

    Google Scholar 

  59. Kumar D, Samantaray SR, Kamwa I, Sahoo NC (2014) Reliability-constrained based optimal placement and sizing of multiple distributed generators in power distribution network using cat swarm optimization. Electr Pow Compon Syst 42:149–164

    Google Scholar 

  60. Zhou J, Shi X, Li X (2016) Utilizing gradient boosted machine for the prediction of damage to residential structures owing to blasting vibrations of open pit mining. J Vib Control 22(19):3986–3997

    Google Scholar 

  61. Abbaszadeh M, Hezarkhani A, Soltani-Mohammadi S (2013) An SVM-based machine learning method for the separation of alteration zones in Sungun porphyry copper deposit. Chem Erde Geochem 73(4):545–554

    Google Scholar 

  62. Soltani-Mohammadi S, Safa M, Mokhtari H (2016) Comparison of particle swarm optimization and simulated annealing for locating additional boreholes considering combined variance minimization. Comput Geosci 95:146–155

    Google Scholar 

  63. Ghorbani A, Hasanzadehshooiili H, Sadowski Ł (2018) Neural prediction of tunnels’ support pressure in elasto-plastic, strain-softening rock mass. Appl Sci 8(5):841. https://doi.org/10.3390/app8050841

    Article  Google Scholar 

  64. Hasanipanah M, Armaghani DJ, Amnieh HB, Koopialipoor M, Arab H (2018) A risk-based technique to analyze flyrock results through rock engineering system. Geotech Geol Eng 36(4):2247–2260

    Google Scholar 

  65. Zhou J, Li E, Yang S, Wang M, Shi X, Yao S et al (2019) Slope stability prediction for circular mode failure using gradient boosting machine approach based on an updated database of case histories. Saf Sci 118:505–518

    Google Scholar 

  66. Sun Y, Zhang J, Li G, Wang Y, Sun J, Jiang C (2019) Optimized neural network using beetle antennae search for predicting the unconfined compressive strength of jet grouting coalcretes. Int J Numer Anal Methods Geomech 43(4):801–813

    Google Scholar 

  67. Zhou J, Li X, Mitri HS (2015) Comparative performance of six supervised learning methods for the development of models of hard rock pillar stability prediction. Nat Hazards 79:291–316

    Google Scholar 

  68. Qi CC, Fourie A (2019) Cemented paste backfill for mineral tailings management: review and future perspectives. Miner Eng 144:106025

    Google Scholar 

  69. Asteris PG et al (2020) A novel heuristic algorithm for the modeling and risk assessment of the COVID-19 pandemic phenomenon. Comput Model Eng Sci 125(2):815–828. https://doi.org/10.32604/cmes.2020.013280

    Article  Google Scholar 

  70. Armaghani DJ, Momeni E, Asteris PG (2020) Application of group method of data handling technique in assessing deformation of rock mass. Metaheuristic Comput Appl 1:1–18. https://doi.org/10.12989/mca.2020.1.1.001

    Article  Google Scholar 

  71. Asteris PG et al (2020) On the metaheuristic models for the prediction of cement-metakaolin mortars compressive strength. Metaheuristic Comput Appl. 5:6. https://doi.org/10.12989/mca.2020.1.1.063

    Article  Google Scholar 

  72. Apostolopoulou M et al (2020) Mapping and holistic design of natural hydraulic lime mortars. Cem Concr Res 136:106167

    Google Scholar 

  73. Hasanipanah M, Amnieh HB (2020) Developing a new uncertain rule-based fuzzy approach for evaluating the blast-induced backbreak. Eng Comput. https://doi.org/10.1007/s00366-019-00919-6

    Article  Google Scholar 

  74. Hasanipanah M, Meng D, Keshtegar B, Trung NT, Thai DK (2020) Nonlinear models based on enhanced Kriging interpolation for prediction of rock joint shear strength. Neural Comput Appl. https://doi.org/10.1007/s00521-020-05252-4

    Article  Google Scholar 

  75. Huang Y, Zhang J, Ann FT, Ma G (2020) Intelligent mixture design of steel fibre reinforced concrete using a support vector regression and firefly algorithm based multi-objective optimization model. Constr Build Mater 260:120457

    Google Scholar 

  76. Zhang J, Huang Y, Aslani F, Ma G, Nener B (2020) A hybrid intelligent system for designing optimal proportions of recycled aggregate concrete. J Clean Prod 273:122922

    Google Scholar 

  77. Zhang J, Huang Y, Wang Y, Ma G (2020) Multi-objective optimization of concrete mixture proportions using machine learning and metaheuristic algorithms. Constr Build Mater 253:119208

    Google Scholar 

  78. Zhang J, Wang Y (2020) Evaluating the bond strength of FRP-to-concrete composite joints using metaheuristic-optimized least-squares support vector regression. Neural Comput Appl. https://doi.org/10.1007/s00521-020-05191-0

    Article  Google Scholar 

  79. Zhang J, Huang Y, Ma G, Sun J, Nener B (2020) A metaheuristic-optimized multi-output model for predicting multiple properties of pervious concrete. Constr Build Mater 249:118803

    Google Scholar 

  80. Zhang J, Sun Y, Li G, Wang Y, Sun J, Li J (2020) Machine-learning-assisted shear strength prediction of reinforced concrete beams with and without stirrups. Eng Comput. https://doi.org/10.1007/s00366-020-01076-x

    Article  Google Scholar 

  81. Ramesh A, Hajihassani M, Rashiddel A (2020) Ground movements prediction in shield-driven tunnels using gene expression programming. Open Constr Build Technol J 14(1):286–297

    Google Scholar 

  82. Zhang J, Wang Y (2020) An ensemble method to improve prediction of earthquake-induced soil liquefaction: a multi-dataset study. Neural Comput Appl. https://doi.org/10.1007/s00521-020-05084-2

    Article  Google Scholar 

  83. Yang Y, Zhang O (1997) A hierarchical analysis for rock engineering using artificial neural networks. Rock Mech Rock Eng 30(4):207–222

    Google Scholar 

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Acknowledgements

This research was funded by the Faculty Start-up Grant of China University of Mining and Technology (Grant No. 102520282).

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Authors and Affiliations

  1. School of Mines, China University of Mining and Technology, Xuzhou, China

    Jiandong Huang

  2. Computational Mechanics Laboratory, School of Pedagogical and Technological Education, 14121 Heraklion, Athens, Greece

    Panagiotis G. Asteris

  3. IMAGEi Consultant Co., Tokyo, Japan

    Siavash Manafi Khajeh Pasha

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

    Ahmed Salih Mohammed

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

    Mahdi Hasanipanah

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  1. Jiandong Huang
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  2. Panagiotis G. Asteris
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Huang, J., Asteris, P.G., Manafi Khajeh Pasha, S. et al. A new auto-tuning model for predicting the rock fragmentation: a cat swarm optimization algorithm. Engineering with Computers 38, 2209–2220 (2022). https://doi.org/10.1007/s00366-020-01207-4

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