PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Developing an XGBoost model to predict blast‑induced peak particle velocity in an open‑pit mine: a case study

Wybrane pełne teksty z tego czasopisma
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Ground vibration is one of the most undesirable effects induced by blasting operations in open-pit mines, and it can cause damage to surrounding structures. Therefore, predicting ground vibration is important to reduce the environmental effects of mine blasting. In this study, an eXtreme gradient boosting (XGBoost) model was developed to predict peak particle velocity (PPV) induced by blasting in Deo Nai open-pit coal mine in Vietnam. Three models, namely, support vector machine (SVM), random forest (RF), and k-nearest neighbor (KNN), were also applied for comparison with XGBoost. To employ these models, 146 datasets from 146 blasting events in Deo Nai mine were used. Performance of the predictive models was evaluated using root-mean-squared error (RMSE) and coefficient of determination (R2). The results indicated that the developed XGBoost model with RMSE = 1.554, R2 = 0.955 on training datasets, and RMSE = 1.742, R2 = 0.952 on testing datasets exhibited higher performance than the SVM, RF, and KNN models. Thus, XGBoost is a robust algorithm for building a PPV predictive model. The proposed algorithm can be applied to other open-pit coal mines with conditions similar to those in Deo Nai.
Czasopismo
Rocznik
Strony
477--490
Opis fizyczny
Bibliogr. 56 poz.
Twórcy
autor
  • Department of Surface Mining, Mining Faculty, Hanoi University of Mining and Geology, 18 Vien St., Duc Thang Ward, Bac Tu Liem Dist., Hanoi, Vietnam
  • Center for Mining, Electro‑Mechanical Research, Hanoi University of Mining and Geology, 18 Vien St., Duc Thang Ward, Bac Tu Liem Dist., Hanoi, Vietnam
  • Department of Surface Mining, Mining Faculty, Hanoi University of Mining and Geology, 18 Vien St., Duc Thang Ward, Bac Tu Liem Dist., Hanoi, Vietnam
  • Center for Mining, Electro‑Mechanical Research, Hanoi University of Mining and Geology, 18 Vien St., Duc Thang Ward, Bac Tu Liem Dist., Hanoi, Vietnam
  • Faculty of Geosciences and Geoengineering, Hanoi University of Mining and Geology, 18 Vien St., Duc Thang Ward, Bac Tu Liem Dist., Hanoi, Vietnam
  • Center for Excellence in Analysis and Experiment, Hanoi University of Mining and Geology, 18 Vien St., Duc Thang Ward, Bac Tu Liem Dist., Hanoi, Vietnam
  • Ministry of Industry and Trade, Hanoi, Vietnam
Bibliografia
  • 1. Ak H, Konuk A (2008) The effect of discontinuity frequency on ground vibrations produced from bench blasting: a case study. Soil Dynamics and Earthquake Engineering 28:686–694
  • 2. Alnaqi AA, Moayedi H, Shahsavar A, Nguyen TK (2019) Prediction of energetic performance of a building integrated photovoltaic/thermal system thorough artificial neural network and hybrid particle swarm optimization models. Energy Convers Manag 183:137–148
  • 3. Altman NS (1992) An introduction to kernel and nearest-neighbor nonparametric regression. The American Statistician 46:175–185
  • 4. Ambraseys NR, Hendron AJ (1968) Dynamic behavior of rock masses. In: Stagg KG, Zienkiewicz OC (eds) Rock mechanics in engineering practices. Wiley, New York, pp 203–207
  • 5. Breiman L (2001) Random forests. Mach Learn 45:5–32
  • 6. Bui X-N, Nguyen H, Le H-A, Bui H-B, Do N-H (2019) Prediction of blast-induced air over-pressure in open-pit mine: assessment of different artificial intelligence. Tech Natl Resour Res. https://doi.org/10.1007/s11053-019-09461-0
  • 7. Chandar KR, Sastry V, Hegde C (2017) A critical comparison of regression models and artificial neural networks to predict ground vibrations. Geotech Geol Eng 35:573–583
  • 8. Chen T, He T (2015) Xgboost: extreme gradient boosting R package version 04-2
  • 9. Chen G, Huang SL (2001) Analysis of ground vibrations caused by open pit production blasts–a case study. Fragblast 5(1–2):91–107
  • 10. Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20:273–297
  • 11. Coursen DL (1995) Method of reducing ground vibration from delay blasting. Google Patents
  • 12. Davies B, Farmer I, Attewell P (1964) Ground vibration from shallow sub-surface blasts. Engineer 217(5644):553–559
  • 13. Drucker H, Burges CJ, Kaufman L, Smola AJ, Vapnik V (1997) Support vector regression machines. In: Advances in neural information processing systems, pp 155–161
  • 14. Duvall WI, Fogelson DE (1962) Review of criteria for estimating damage to residences from blasting vibrations. US Department of the Interior, Bureau of Mines
  • 15. Faradonbeh RS, Monjezi M (2017) Prediction and minimization of blast-induced ground vibration using two robust meta-heuristic algorithms. Eng Comput 33(4):835–851. https://doi.org/10.1007/s00366-017-0501-6
  • 16. Faradonbeh RS, Armaghani DJ, Majid MA, Tahir MM, Murlidhar BR, Monjezi M, Wong H (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 13:1453–1464
  • 17. Friedman JH (2001) Greedy function approximation: a gradient boosting machine. Ann Stat 29(5):1189–1232
  • 18. Friedman JH (2002) Stochastic gradient boosting. Comput Stat Data Anal 38:367–378
  • 19. Friedman J, Hastie T, Tibshirani R (2000) Additive logistic regression: a statistical view of boosting (with discussion and a rejoinder by the authors). Ann Stat 28:337–407
  • 20. Friedman J, Hastie T, Tibshirani R (2001) The elements of statistical learning vol 1. vol 10. Springer series in statistics New York, NY, USA
  • 21. Gad EF, Wilson JL, Moore AJ, Richards AB (2005) Effects of mine blasting on residential structures Journal of performance of constructed facilities 19:222–228
  • 22. Gao W, Dimitrov D, Abdo H (2018a) Tight independent set neighborhood union condition for fractional critical deleted graphs and ID deleted graphs. Discrete Continuous Dyn Syst S 123–144. https://doi.org/10.3934/dcdss.2019045
  • 23. Gao W, Guirao JL, Basavanagoud B, Wu J (2018b) Partial multi-dividing ontology learning algorithm. Inf Sci 467:35–58
  • 24. Gao W, Guirao JLG, Abdel-Aty M, Xi W (2018c) An independent set degree condition for fractional critical deleted graphs. Discrete Continuous Dyn Syst S 12:877–886. https://doi.org/10.3934/dcdss.2019058
  • 25. Gao W, Wang W, Dimitrov D, Wang Y (2018d) Nano properties analysis via fourth multiplicative ABC indicator calculating. Arab J Chem 11:793–801
  • 26. Gao W, Wu H, Siddiqui MK, Baig AQ (2018e) Study of biological networks using graph theory. Saudi J Biol Sci 25:1212–1219
  • 27. Ghasemi E, Ataei M, Hashemolhosseini H (2013) Development of a fuzzy model for predicting ground vibration caused by rock blasting in surface mining. J Vib Control 19:755–770
  • 28. Ghasemi E, Kalhori H, Bagherpour R (2016) A new hybrid ANFIS–PSO model for prediction of peak particle velocity due to bench blasting. Eng Comput 32:607–614
  • 29. 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
  • 30. Hajihassani M, Armaghani DJ, Marto A, Mohamad ET (2015) Ground vibration prediction in quarry blasting through an artificial neural network optimized by imperialist competitive algorithm. Bull Eng Geol Environ 74:873–886
  • 31. 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
  • 32. Hasanipanah M, Armaghani DJ, Amnieh HB, Majid MZA, Tahir MM (2017a) Application of PSO to develop a powerful equation for prediction of flyrock due to blasting. Neural Comput Appl 28:1043–1050
  • 33. Hasanipanah M, Faradonbeh RS, Amnieh HB, Armaghani DJ, Monjezi M (2017b) Forecasting blast-induced ground vibration developing a CART model. Eng Comput 33:307–316
  • 34. Hasanipanah M, Golzar SB, Larki IA, Maryaki MY, Ghahremanians T (2017c) Estimation of blast-induced ground vibration through a soft computing framework. Eng Comput 33(4):951–959. https://doi.org/10.1007/s00366-017-0508-z
  • 35. Hu C, Jain G, Zhang P, Schmidt C, Gomadam P, Gorka T (2014) Data-driven method based on particle swarm optimization and k-nearest neighbor regression for estimating capacity of lithium-ion battery. Appl Energy 129:49–55
  • 36. Longjun D, Xibing L, Ming X, Qiyue L (2011) Comparisons of random forest and support vector machine for predicting blasting vibration characteristic parameters. Procedia Eng 26:1772–1781
  • 37. Moayedi H, Nazir R (2018) Malaysian experiences of peat stabilization, state of the art. Geotech Geol Eng 36:1–11
  • 38. Moayedi H, Rezaei A (2017) An artificial neural network approach for under-reamed piles subjected to uplift forces in dry sand. Neural Comput Appl. https://doi.org/10.1007/s00521-017-2990-z
  • 39. Moayedi H, Raftari M, Sharifi A, Jusoh WAW, Rashid ASA (2019) Optimization of ANFIS with GA and PSO estimating α ratio in driven piles. Eng Comput. https://doi.org/10.1007/s00366-018-00694-w
  • 40. Monjezi M, Bahrami A, Varjani AY, Sayadi AR (2011a) Prediction and controlling of flyrock in blasting operation using artificial neural network. Arab J Geosci 4:421–425
  • 41. Monjezi M, Ghafurikalajahi M, Bahrami A (2011b) Prediction of blast-induced ground vibration using artificial neural networks. Tunn Undergr Space Technol 26:46–50
  • 42. Monjezi M, Hasanipanah M, Khandelwal M (2013) Evaluation and prediction of blast-induced ground vibration at Shur River Dam, Iran, by artificial neural network. Neural Comput Appl 22:1637–1643
  • 43. Nguyen H, Bui X-N (2018a) A comparison of artificial neural network and empirical technique for predicting blast-induced ground vibration in open-pit mine. In: Mining sciences and technology—XXVI, Mong Cai, Hanoi, Vietnam. Industry and trade of the socialist Republic of Vietnam, pp 177–182
  • 44. Nguyen H, Bui X-N (2018b) Predicting blast-induced air overpressure: a robust artificial intelligence system based on artificial neural networks and random forest. Natl Resour Res. https://doi.org/10.1007/s11053-018-9424-1
  • 45. Nguyen H, Bui X-N, Bui H-B, Mai N-L (2018a) 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
  • 46. Nguyen H, Bui X-N, Tran Q-H, Le T-Q, Do N-H, Hoa LTT (2018b) Evaluating and predicting blast-induced ground vibration in open-cast mine using ANN: a case study in Vietnam SN. Appl Sci 1:125. https://doi.org/10.1007/s42452-018-0136-2
  • 47. Nguyen H, Bui X-N, Tran Q-H, Mai N-L (2019) A new soft computing model for estimating and controlling blast-produced ground vibration based on hierarchical k-means clustering and cubist algorithms. Appl Soft Comput. https://doi.org/10.1016/j.asoc.2019.01.042
  • 48. Protodiakonov M, Koifman M, Chirkov S, Kuntish M, Tedder R (1964) Rock strength passports and methods for their determination. Nauka, Moscow
  • 49. Roy PP (1991) Prediction and control of ground vibration due to blasting. Colliery Guard 239:215–219
  • 50. Saadat M, Khandelwal M, Monjezi M (2014) An ANN-based approach to predict blast-induced ground vibration of Gol-E-Gohar iron ore mine. Iran J Rock Mech Geotech Eng 6:67–76
  • 51. 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. https://doi.org/10.1007/s00366-017-0546-6
  • 52. Standard I (1973) Criteria for safety and design of structures subjected to under ground blast ISI, IS-6922
  • 53. Taheri K, Hasanipanah M, Golzar SB, Majid MZA (2017) A hybrid artificial bee colony algorithm-artificial neural network for forecasting the blast-produced ground vibration. Eng Comput 33:689–700
  • 54. Vigneau E, Courcoux P, Symoneaux R, Guérin L, Villière A (2018) Random forests: a machine learning methodology to highlight the volatile organic compounds involved in olfactory perception. Food Qual Prefer 68:135–145
  • 55. Vinacomin (2015) Report on geological exploration of Coc Sau open pit coal mine, Quang Ninh, Vietnam (in Vietnamse-unpublished). VINACOMIN, Vietnam
  • 56. Zorlu K, Gokceoglu C, Ocakoglu F, Nefeslioglu H, Acikalin S (2008) Prediction of uniaxial compressive strength of sandstones using petrography-based models. Eng Geol 96:141–158
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020). Korekta do artykułu w Acta Geophysica 2021 Vol. 69, no. 2. Nr DOI korekty: 10.1007/s11600-021-00575-9
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-eb8687fc-9205-4fba-b378-b77cbb947654
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.