All Issue

2018 Vol.28, Issue 6 Preview Page
December 2018. pp. 651-669
Abstract

본 연구에서는 유전자 프로그래밍과 개체군집최적화기법을 이용하여 픽 커터의 비에너지를 예측하기 위한 모델을 제안하였다. 기계굴착장비의 굴진성능을 평가하는 것은 터널의 설계 초기 단계에서 매우 중요하며, 비에너지를 이용한 기계 굴착장비의 굴진성능평가방법은 모든 기계굴착공법에 적용될 수 있는 표준화된 방법이다. 본 연구에서는 코니컬형상의 픽 커터가 암석을 절삭할 때 요구되는 비에너지와 암석의 강도특성, 절삭조건 간의 상관관계를 분석하고자 하였으며, 선행연구를 통해 총46개의 선형절삭시험 결과를 수집하여 분석에 활용하였다. 본 연구에서 제안한 예측모델을 이용하여 산정된 픽 커터의 비에너지는 다중선형회귀분석에 비해 작은 평균제곱오차를 나타내었으며, 결정계수 또한 본 연구에서 제안한 모델이 다중선형회귀분석에 비해 우수한 예측결과를 나타내는 것을 확인할 수 있었다.

This study suggests the prediction model to estimate the specific energy of a pick cutter using a gene expression programming (GEP) and particle swarm optimization (PSO). Estimating the performance of mechanical excavators is of crucial importance in early design stage of tunnelling projects, and the specific energy (SE) based approach serves as a standard performance prediction procedure that is applicable to all excavation machines. The purpose of this research, is to investigate the relationship between UCS and BTS, penetration depth, cut spacing, and SE. A total of 46 full-scale linear cutting test results using pick cutters and different values of depth of cut and cut spacing on various rock types was collected from the previous study for the analysis. The Mean Squared Error (MSE) associated with the conventional Multiple Linear Regression (MLR) method is more than two times larger than the MSE generated by GEP-PSO algorithm. The R2 value associated with the GEP-PSO algorithm, is about 0.13 higher than the R2 associated with MLR.

References
  1. Balci, C., M.A., Demircin, H., Copur and H., Tuncdemir, 2004, Estimation of optimum specific energy based on rock properties for assessment of roadheader performance, J. South African Inst. Min. Metall. 104, 633-641.
  2. Bilgin, N., H., Copur and C., Balci, 2013, Mechanical Excavation in Mining and Civil Industries, CRC Press, London, 89p.
  3. Çanakcı, H., A., Baykasoğlu and H., Güllü, 2009, Prediction of compressive and tensile strength of Gaziantep basalts via neural networks and gene expression programming, Neural Comput. Appl. 18, 1031.10.1007/s00521-008-0208-0
  4. Copur, H., C., Balci, N., Bilgin, D., Tumac and E., Avunduk, 2012, Predicting cutting performance of chisel tools by using physical and mechanical properties of natural stones, Proc. of European Rock Mechanics Symposium 2012, May, Stockholm.
  5. Copur, H., N., Bilgin, H., Tuncdemir and C., Balci, 2003, A set of indices based on indentation tests for assessment of rock cutting performance and rock properties, J. South African Inst. Min. Metall, 589-600.
  6. Ferreira, C., 2006, Gene expression programming: mathematical modeling by an artificial intelligence, Springer, Berlin.10.1007/3-540-32849-1_2
  7. He, X. and C., Xu, 2016, Specific energy as an index to identify the critical failure mode transition depth in rock cutting, Rock Mech. Rock Eng. 49, 1461-1478.10.1007/s00603-015-0819-6
  8. Jeong, H.Y., S.W., Jeon, J.W., Cho, S.H., Chang and G.J., Bae, 2011, Assessment of cutting performance of a TBM disc cutter for anisotropic rock by linear cutting test. Tunn. Undergr. Sp. 21, 508-517.
  9. Jeong, H.Y., S.W., Jeon and J.W., Cho, 2012, A study on punch penetration test for performance estimation of tunnel boring machine, Tunn. Undergr. Sp. 22, 144-156.10.7474/TUS.2012.22.2.144
  10. Jeong, H.Y., 2017, Assessment of rock cutting efficiency of pick cutters for the optimal design of a mechanical excavator, Ph. D thesis, Seoul National University, Seoul.
  11. Jeong, H.Y. and S.W., Jeon, 2018, Characteristics of size distribution of rock chip produced by rock cutting with a pick cutter, Geomechanics and Engineering. 15.3, 811-822.
  12. Kennedy, J. and R., Eberhart, 1995, Particle swarm optimization, Proc. ICNN’95 - International Conference on Neural Networks, Vol. 4, 1942-1948.10.1109/ICNN.1995.488968
  13. Khandelwal, M., D.J., Armaghani, R.S., Faradonbeh, P.G., Ranjith and and S., Ghoraba, 2016, A new model based on gene expression programming to estimate air flow in a single rock joint, Environ. Earth Sci. 75, 739.10.1007/s12665-016-5524-6
  14. Matlab, 2017, Version R2017b, The MathWorks, Inc.,Natick, Massachusetts, United States.
  15. Pomeroy, C.D., 1963, The breakage of coal by wedge action; factors influencing breakage by any given shape of tool, Colliery Guard, November, 672-677.
  16. Robbins, R.J., 2000, Mechanization of underground mining: a quick look backward and forward, Int. J. Rock Mech. Min. Sci. 37, 413-421.10.1016/S1365-1609(99)00116-1
  17. Rostami, J., 2011, Mechanical Rock Breaking, in: SME Mining Engineering Handbook, Third Edition, Dearbon, 388p.
  18. Rostami, J., L., Ozdemir and D.M., Neil, 1994, Performance prediction: a key issue in mechanical hard rock mining, Min. Eng. 46, 1263-1267.
  19. Roxborough, F.F., 1973, Cutting rock with picks, Min. Eng. 132, 445-454.
  20. Faradonbeh, R.S., D., Jahed and M., Monjezi, 2016, Genetic programming and gene expression programming for flyrock assessment due to mine blasting, Int. J. Rock Mech. Min. Sci. 88, 254-264.10.1016/j.ijrmms.2016.07.028
  21. Faradonbeh, R.S., A., Salimi, M., Monjezi, A., Ebrahimabadi, and C., Moormann, 2017, Roadheader performance prediction using genetic programming (GP) and gene expression programming (GEP) techniques, Environ. Earth Sci. 76, 584.10.1007/s12665-017-6920-2
  22. Terezopoulos, N.G., 1987, Influence of geotechnical environments on mine tunnel drivage performance, in: Advances in Mining Science and Technology. Elsevier, Amsterdam, pp. 139-156.
  23. Thuro, K. and R.J., Plinninger, 2003, Hard rock tunnel boring, cutting, drilling and blasting: rock parameters for excavatability, Proc. 10th of Int. Congr. on Rock Mech., September, Sandton.
  24. Wang, X., Q.F., Wang, Y.P., Liang, O., Su and L., Yang, 2018, Dominant Cutting Parameters Affecting the Specific Energy of Selected Sandstones when Using Conical Picks and the Development of Empirical Prediction Models, Rock Mech. Rock Eng. 51.10, 1-18.10.1007/s00603-018-1522-1
Information
  • Publisher :Korean Society for Rock Mechanics and Rock Engineering
  • Publisher(Ko) :한국암반공학회
  • Journal Title :Tunnel and Underground Space
  • Journal Title(Ko) :터널과 지하공간
  • Volume : 28
  • No :6
  • Pages :651-669
  • Received Date :2018. 12. 04
  • Accepted Date : 2018. 12. 26