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A novel solution for simulating air overpressure resulting from blasting using an efficient cascaded forward neural network

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Abstract

Air overpressure (AOp) is a hazardous effect induced by the blasting method in surface mines. Therefore, it needs to be predicted to reduce the potential risk of damage. The aim of this study is to offer an efficient method to predict AOp using a cascaded forward neural network (CFNN) trained by Levenberg–Marquardt (LM) algorithm, called the CFNN-LM model. Additionally, a generalized regression neural network (GRNN) and extreme learning machine (ELM) were employed to demonstrate the accuracy level of the proposed CFNN-LM model. To conduct the CFNN-LM, GRNN, and ELM models, an extensive database, related to four quarry sites in Malaysia, was used including 62 sets of dependent and independent parameters. Next, the performances of the aforementioned models were checked and discussed through statistical criteria and efficient graphical tools. Finally, the results showed the superiority of CFNN-LM (R2 = 0.9263 and RMSE = 3.0444) over GRNN (R2 = 0.7787 and RMSE = 5.1211) and ELM (R2 = 0.6984 and RMSE = 6.2537) models in terms of prediction accuracy. Furthermore, three different regression analysis metrics were used to perform the sensitivity analysis, and according to the obtained results, the maximum charge per delay (\(\beta\) = 0.475, SE = 0.115, t-test = 4.125) was considered as the most influential feature in modeling the AOp.

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References

  1. Fang Q, Nguyen H, Bui XN et al (2020) Estimation of blast-induced air overpressure in quarry mines using cubist-based genetic algorithm. Nat Resour Res 29:593–607

    Google Scholar 

  2. Lonsbury-Martin BL, Harris FP, Hawkins MD, Stagner BB, Martin GK (1990) Distortion product emissions in humans: I. Basic properties in normally hearing subjects. Ann Otol Rhinol Laryngol 99(5):3–14

    Google Scholar 

  3. 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. https://doi.org/10.1007/s00521-018-3717-5

    Article  Google Scholar 

  4. Remennikov AM, Rose TA (2007) Predicting the effectiveness of blast wall barriers using neural networks. Int J Impact Eng 34(12):1907–1923

    Google Scholar 

  5. Armaghani DJ, Hasanipanah M, Mahdiyar A, Abd Majid MZ, Bakhshandeh Amnieh H, Tahir MMD (2018) Airblast prediction through a hybrid genetic algorithm-ANN model. Neural Comput Appl 29(9):619–629

    Google Scholar 

  6. Siskind DE, Stachura VJ, Stagg MS, Kopp JW (1980) Structure response and damage produced by airblast from surface mining, Citeseer

  7. Loder B (1987) National association of Australian state road authorities, in: Australian Workshop for Senior ASEAN Transport Officials, 1985, Canberra

  8. McKenzie C (1990) Quarry blast monitoring: technical and environmental perspectives. Quarry Manag 17:23–24

    Google Scholar 

  9. Khandelwal M, Singh TN (2005) Prediction of blast induced air overpressure using neural network. Noise Vib Worldw 36(2):7–16

    Google Scholar 

  10. Keshtegar B, Hasanipanah M, Bakhshayeshi I, Sarafraz ME (2019) A novel nonlinear modeling for the prediction of blast-induced airblast using a modified conjugate FR method. Measurement 131:35–41

    Google Scholar 

  11. AminShokravi A, Eskandar H, Derakhsh AM et al (2018) The potential application of particle swarm optimization algorithm for forecasting the air-overpressure induced by mine blasting. Eng Comput 34:277–285

    Google Scholar 

  12. Nguyen H, Bui XN (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

    MathSciNet  Google Scholar 

  13. Ozdemir B, Kumral M (2019) A system-wide approach to minimize the operational cost of bench production in open-cast mining operations. Int J Coal Sci Technol 6(1):84–94

    Google Scholar 

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

    Google Scholar 

  15. 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 

  16. 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 

  17. 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 

  18. 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 

  19. 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 

  20. Yang H, Hasanipanah M, Tahir MM, Bui DT (2020) Intelligent prediction of blasting-induced ground vibration using ANFIS optimized by GA and PSO. Nat Resour Res 29:739–750

    Google Scholar 

  21. Liao X, Khandelwal M, Yang H, Koopialipoor M, Murlidhar BR (2020) Effects of a proper feature selection on prediction and optimization of drilling rate using intelligent techniques. Eng Comput 36(2):499–510

    Google Scholar 

  22. 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 

  23. Huang J, Duan T, Zhang Y, Liu J, Zhang J, Lei Y (2020) Predicting the permeability of pervious concrete based on the beetle antennae search algorithm and random forest model. Adv Civ Eng 2:8863181

    Google Scholar 

  24. Jamei M, Pourrajab R, Ahmadianfar I, Noghrehabadi A (2020) Accurate prediction of thermal conductivity of ethylene glycol-based hybrid nanofluids using artificial intelligence techniques. Int Commun Heat Mass Transf 116:104624

    Google Scholar 

  25. Jamei M, Ahmadianfar I, Chu X, Yaseen ZM (2020) Prediction of surface water total dissolved solids using hybridized wavelet-multigene genetic programming: new approach. J Hydrol 589:125335

    Google Scholar 

  26. Jamei M, Ahmadianfar I (2020) A rigorous model for prediction of viscosity of oil-based hybrid nanofluids. Phys A 556:124827

    Google Scholar 

  27. Ahmadianfar I, Jamei M, Chu X (2020) A novel hybrid wavelet-locally weighted linear regression (W-LWLR) model for electrical conductivity (EC) prediction in surface water. J Contam Hydrol 232:103641

    Google Scholar 

  28. 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 

  29. 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

    Google Scholar 

  30. Huang J, Sun Y, Zhang J (2021) Reduction of computational error by optimizing SVR kernel coefficients to simulate concrete compressive strength through the use of a human learning optimization algorithm. Eng Comput. https://doi.org/10.1007/s00366-021-01305-x

    Article  Google Scholar 

  31. Jamei M, Ahmadianfar I, Chu X, Yaseen ZM (2021) Estimation of triangular side orifice discharge coefficient under a free flow condition using data-driven models. Flow Meas Instrum 77:101878

    Google Scholar 

  32. Khandelwal M, Singh TN (2005) Prediction of blast induced air overpressure using neural network. Noise VibWorldw 36(2):7–16

    Google Scholar 

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

    Google Scholar 

  34. 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 

  35. 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

    Google Scholar 

  36. Hasanipanah M, Shahnazar A, Amnieh HB, Armaghani DJ (2017) Prediction of air-overpressure caused by mine blasting using a new hybrid PSO–SVR model. Eng Comput 33(1):23–31

    Google Scholar 

  37. Zhou J, Nekouie A, Arslan CA, Pham BT, Hasanipanah M (2019) Novel approach for forecasting the blast induced AOp using a hybrid fuzzy system and firefly algorithm. Eng Comput. https://doi.org/10.1007/s00366-019-00725-0

    Article  Google Scholar 

  38. Nguyen H, Bui XN (2020) Soft computing models for predicting blast-induced air over-pressure: a novel artificial intelligence approach. Appl Soft Comput 92:106292

    Google Scholar 

  39. Gravetter FJ, Wallnau LB, Forzano L-AB, Witnauer JE (2020) Essentials of statistics for the behavioral sciences. Cengage Learning

  40. Jamei M, Ahmadianfar I, Olumegbon IA, Karbasi M, Asadi A (2020) On the assessment of specific heat capacity of nanofluids for solar energy applications: application of Gaussian process regression (GPR) approach. J Energy Storage 12:102067

    Google Scholar 

  41. Nait Amar M (2020) Modeling solubility of sulfur in pure hydrogen sulfide and sour gas mixtures using rigorous machine learning methods. Int J Hydrog Energy 45:33274–33287. https://doi.org/10.1016/j.ijhydene.2020.09.145

    Article  Google Scholar 

  42. Abujazar MSS, Fatihah S, Ibrahim IA, Kabeel AE, Sharil S (2018) Productivity modelling of a developed inclined stepped solar still system based on actual performance and using a cascaded forward neural network model. J Clean Prod 170:147–159

    Google Scholar 

  43. Hemmati-Sarapardeh A, Nait Amar M, Soltanian MR, Dai Z, Zhang X (2020) Modeling CO2 solubility in water at high pressure and temperature conditions. Energy Fuels 34:4761–4776

    Google Scholar 

  44. Hemmati-Sarapardeh A, Varamesh A, Husein MM, Karan K (2018) On the evaluation of the viscosity of nanofluid systems: modeling and data assessment. Renew Sustain Energy Rev 81:313–329. https://doi.org/10.1016/j.rser.2017.07.049

    Article  Google Scholar 

  45. Specht DF (1991) A general regression neural network. IEEE Trans Neural Netw 2:568–576. https://doi.org/10.1109/72.97934

    Article  Google Scholar 

  46. Mehrjoo H, Riazi M, Nait Amar M, Hemmati-Sarapardeh A (2020) Modeling interfacial tension of methane-brine systems at high pressure and high salinity conditions. J Taiwan Inst Chem Eng. https://doi.org/10.1016/j.jtice.2020.09.014

    Article  Google Scholar 

  47. Feng Y, Gong D, Mei X, Cui N (2017) Estimation of maize evapotranspiration using extreme learning machine and generalized regression neural network on the China Loess Plateau. Hydrol Res 48:1156–1168

    Google Scholar 

  48. Singh R, Vishal V, Singh TN, Ranjith PG (2013) A comparative study of generalized regression neural network approach and adaptive neuro-fuzzy inference systems for prediction of unconfined compressive strength of rocks. Neural Comput Appl 23:499–506

    Google Scholar 

  49. Kisi O (2008) The potential of different ANN techniques in evapotranspiration modelling. Hydrol Process An Int J 22:2449–2460

    Google Scholar 

  50. KISI Ö, (2006) Generalized regression neural networks for evapotranspiration modelling. Hydrol Sci J 51:1092–1105

    Google Scholar 

  51. Huang G-B, Zhu Q-Y, Siew C-K (2006) Extreme learning machine: theory and applications. Neurocomputing 70:489–501

    Google Scholar 

  52. Mahdaviara M, Nait Amar M, Ghazanfari MH, Hemmati-Sarapardeh A (2020) Modeling relative permeability of gas condensate reservoirs: advanced computational frameworks. J Pet Sci Eng 189:106929

    Google Scholar 

  53. Huang G-B, Siew C-K (2005) Extreme learning machine with randomly assigned RBF kernels. Int J Inf Technol 11:16–24

    Google Scholar 

  54. Yaseen ZM, Deo RC, Hilal A, Abd AM, Bueno LC, Salcedo-Sanz S et al (2018) Predicting compressive strength of lightweight foamed concrete using extreme learning machine model. Adv Eng Softw 115:112–125

    Google Scholar 

  55. Huang G, Huang G-B, Song S, You K (2015) Trends in extreme learning machines: a review. Neural Netw 61:32–48

    MATH  Google Scholar 

  56. 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 

  57. 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 1:5198583. https://doi.org/10.1155/2019/5198583

    Article  Google Scholar 

  58. 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 

  59. 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 

  60. 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 

  61. 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 

  62. Yang H, Koopialipoor M, Armaghani DJ, Gordan B, Khorami M, Tahir MM (2020) Intelligent design of retaining wall structures under dynamic conditions. Steel Compos Struct 31(6):629–640

    Google Scholar 

  63. Huang J, Zhang J, Ren J, Chen H (2021) Anti-rutting performance of the damping asphalt mixtures (DAMs) made with a high content of asphalt rubber (AR). Constr Build Mater 271:121878

    Google Scholar 

  64. 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 

  65. 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 

  66. Yang HQ, Li Z, Jie TQ, Zhang ZQ (2018) Effects of joints on the cutting behavior of disc cutter running on the jointed rock mass. Tunn Undergr Space Technol 81:112–120

    Google Scholar 

  67. Huang J, Kumar GS, Sun Y (2021) Evaluation of workability and mechanical properties of asphalt binder and mixture modified with waste toner. Constr Build Mater 276:122230

    Google Scholar 

  68. 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 

  69. 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 

  70. 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 

  71. Fox J (1997) Applied regression analysis, linear models, and related methods. Sage Publications Inc, Thousand Oaks

    Google Scholar 

  72. Bates DM, Watts DG (1988) Nonlinear regression analysis and its applications, vol 2. Wiley, New York

    MATH  Google Scholar 

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Acknowledgements

Supported by the Science and Technology Research Program of Chongqing Municipal Education Commission (Grant NO. KJQN201804305, KJQN201904307).

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Authors and Affiliations

  1. Department of Transportation and Municipal Engineering, Chongqing Jianzhu College, Chongqing, 400072, China

    Jie Zeng

  2. Faculty of Engineering, Shohadaye Hoveizeh University of Technology, Dasht-e Azadegan, Susangerd, Iran

    Mehdi Jamei

  3. Département Études Thermodynamiques, Division Laboratoires, Sonatrach, Boumerdes, Algeria

    Menad Nait Amar

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

    Mahdi Hasanipanah

  5. Department of Mining, Faculty of Engineering, Tarbiat Modares University, 14115-143, Tehran, Iran

    Parichehr Bayat

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  1. Jie Zeng
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Correspondence to Mahdi Hasanipanah.

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Zeng, J., Jamei, M., Nait Amar, M. et al. A novel solution for simulating air overpressure resulting from blasting using an efficient cascaded forward neural network. Engineering with Computers 38 (Suppl 3), 2069–2081 (2022). https://doi.org/10.1007/s00366-021-01381-z

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