Application of Soft Computing Methods for the Estimation of Roadheader Performance from Schmidt Hammer Rebound Values

Document Type : Research Article

Author

Dept. of Mining, Arak University of Technology, Iran

Abstract

Estimation of roadheader performance is one of the main topics in determining the economics of underground excavation projects. The poor performance estimation of roadheader scan leads to costly contractual claims. In this paper, the application of soft computing methods for data analysis called adaptive neuro-fuzzy inference system- subtractive clustering method (ANFIS-SCM) and artificial  neural  network  (ANN) optimized  by  hybrid  particle  swarm  optimization  and  genetic  algorithm  (HPSOGA) to estimate roadheader performance is demonstrated. The data to show the applicability of these methods were collected from tunnels for Istanbul’s sewage system, Turkey. Two estimation models based on ANFIS-SCM and ANN-HPSOGA were developed. In these models, Schmidt hammer rebound values and rock quality designation (RQD) were utilized as the input parameters, and net cutting rates constituted the output parameter. Various statistical performance indices were used to compare the performance of those estimation models. The results indicated that the ANFIS-SCM model has strong potentials to estimate roadheader performance with high degrees of accuracy and robustness.

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References

1. Salsani, A., et al., Predicting roadheader performance by using artificial neural network. Neural Computing and Applications, 2014. 24(7-8): p. 1823-1831.
2. Abdolreza, Y.-C., et al., A new model to predict roadheader performance using rock mass properties. Journal of Coal Science and Engineering (China), 2013. 19(1): p. 51-56.
3. Iphar, M., ANN and ANFIS performance prediction models for hydraulic impact hammers. Tunnelling and Underground Space Technology, 2012. 27(1): p. 23-29.
4. Uehigashi, K., et al. Possibility of rock excavation by boom-type tunneling machines.  6th Australian Tunneling Conference. Melbourne1987. p. 253–259.
5. Schneider, H., Criteria for selecting a boom-type roadheader: Min MagSept 1988, P183–187. International Journal of Rock Mechanics and Mining Sciences & Geomechanics Abstracts, 1989. 26(2): p. 78-79.
6. Gehring, K. H., A cutting comparison. Tunnels and Tunnelling, 1989. 21: p. 27-30.
7. Rostami, J., et al. Performance prediction: a key issue in mechanical hard rock mining.  International Journal of Rock Mechanics and Mining Science & Geomechanics Abstracts1995. p. 171A-171A.
8. Thuro, K., et al., Predicting roadheader advance rates. Tunnels & Tunnelling International, 1999. 6: p. 36-39.
9. Tumac, D., et al., Estimation of rock cuttability from shore hardness and compressive strength properties. Rock Mechanics and Rock Engineering, 2007. 40(5): p. 477-490.
10. Madan, M. Underground excavation with road headerscase studies.  World Tunnel Congress2008. p. 1073-1084.
11. Goshtasbi, K., et al., Evaluation of boring machine performance with special reference to geomechanical characteristics. International Journal of Minerals, Metallurgy and Materials, 2009. 16(6): p. 615-619.
12. Sandbak, L. A. Road header drift excavation and geotechnical rock classification at San Manuel, Arizona.  Proceedings of the Rapid Excavation and Tunnelling Conference. New York1985. p. 902–916.
13. Douglas, W., Roadheaders open new horizons at San Manuel. Engineering & Mining Journal, 1985. 186(8): p. 22-25.
14. Bilgin, N., ., et al., Roadheader performance in Istanbul, Golden Horn clean-up contributes valuable data. Tunnels & Tunneling, 1988. 6: p. 41-44.
15. Bilgin, N., ., et al., Roadheaders clean valuable tips for Istanbul Metro. Tunnels & Tunneling, 1990. 10: p. 29-32.
16. Ebrahimabadi, A., et al., Prediction of roadheaders' performance using artificial neural network approaches (MLP and KOSFM). Journal of Rock Mechanics and Geotechnical Engineering, 2015. 7(5): p. 573-583.
17. Fowel, R. J., et al. Cuttability assessment applied to drag tool tunnelling machines.  Proceeding of the 7th International Congress on Rock Mechanics, ISRM. Aachen.
18. Copur, H., et al., Roadheader applications in mining and tunneling industries. Mining Engineering, 1998. 50: p. 38-42.
19. Bilgin, N., et al., Some geological and geotechnical factors affecting the performance of a roadheader in an inclined tunnel. Tunnelling and Underground Space Technology, 2004. 19(6): p. 629-636.
20. Ebrahimabadi, A., et al., Predictive models for roadheaders’ cutting performance in coal measure rocks. Yerbilimleri, 2011. 32(2): p. 89-104.
21. Ebrahimabadi, A., et al., A model to predict the performance of roadheaders based on the Rock Mass Brittleness Index. South African Institute of Mining and Metallurgy Journal, 2011. 111(5): p. 355-364.
22. Khalaj, G., et al., Artificial neural network to predict the effect of heat treatments on Vickers microhardness of low-carbon Nb microalloyed steels. Neural Computing and Applications, 2013. 22(5): p. 879-888.
23. Hornik, K., et al., Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networks. Neural Networks, 1990. 3(5): p. 551-560.
24. García-Pedrajas, N., et al., COVNET: a cooperative coevolutionary model for evolving artificial neural networks. IEEE Transactions on Neural Networks, 2003. 14(3): p. 575-596.
25. Ahmadi, M. H., et al., Prediction of power in solar stirling heat engine by using neural network based on hybrid genetic algorithm and particle swarm optimization. Neural Computing and Applications, 2013. 22(6): p. 1141-1150.
26. Holland John, H. Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence: MIT Press Cambridge, MA, USA, 1975.
27. Wang, C., et al., Identification of dynamic rock properties using a genetic algorithm. International Journal of Rock Mechanics and Mining Sciences, 2004. 41: p. 490-495.
28. Osman, M., et al., A combined genetic algorithm-fuzzy logic controller (GA–FLC) in nonlinear programming. Applied Mathematics and Computation, 2005. 170(2): p. 821-840.
29. Hassan, R., et al. A comparison of particle swarm optimization and the genetic algorithm.  Proceedings of the 1st AIAA multidisciplinary design optimization specialist conference2005. p. 18-21.
30. Majdi, A., et al., Evolving neural network using a genetic algorithm for predicting the deformation modulus of rock masses. International Journal of Rock Mechanics and Mining Sciences, 2010. 47(2): p. 246-253.
31. Eberhart, R., et al. A new optimizer using particle swarm theory.  Micro Machine and Human Science, 1995 MHS'95, Proceedings of the Sixth International Symposium on: IEEE, 1995. p. 39-43.
32. Chen, S.-F. Redundant Feature Selection Based on Hybrid GA and BPSO.  Communication Software and Networks (ICCSN), 2011 IEEE 3rd International Conference on: IEEE, 2011. p. 414-418.
33. Shi, Y., et al. A modified particle swarm optimizer.  Evolutionary Computation Proceedings, IEEE World Congress on Computational Intelligence: IEEE, 1998. p. 69-73.
34. Fattahi, H., Application of improved support vector regression model for prediction of deformation modulus of a rock mass. Engineering with Computers, 2016. 32(4): p. 567-580.
35. Kaveh, A., et al., Hybrid genetic algorithm and particle swarm optimization for the force method-based simultaneous analysis and design. Iranian Journal of Science and Technology, Transaction B: Engineering, 2010. 34(B1): p. 15-34.
36. Juang, C.-F., A hybrid of genetic algorithm and particle swarm optimization for recurrent network design. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 2004. 34(2): p. 997-1006.
37. Fattahi, H., Indirect estimation of deformation modulus of an in situ rock mass: an ANFIS model based on grid partitioning, fuzzy c-means clustering and subtractive clustering. Geosciences Journal, 2016. 20(5): p. 681-690.
38. Fattahi, H., Applying soft computing methods to predict the uniaxial compressive strength of rocks from schmidt hammer rebound values. Computational Geosciences, 2017. 21(4): p. 665-681.
39. Chiu, S. L., Fuzzy model identification based on cluster estimation. Journal of intelligent and Fuzzy systems, 1994. 2(3): p. 267-278.
40. Shahriar, K., Rock cuttability and geotechnical factors affecting the penetration rates of roadheaders. Ph D Thesis, Istanbul Technical University, 1988. p. 241.
41. Goktan, R., et al., A comparative study of Schmidt hammer testing procedures with reference to rock cutting machine performance prediction. International Journal of Rock Mechanics and Mining Sciences, 2005. 42(3): p. 466-472.
42. Poole, R., et al., Consistency and repeatability of Schmidt hammer rebound data during field testing. International Journal of Rock Mechanics and Mining Sciences & Geomechanics Abstracts, 1980. 17(3): p. 167-171.
43. Hucka, V., A rapid method of determining the strength of rocks in situ. International Journal of Rock Mechanics and Mining Sciences & Geomechanics Abstracts, 1965. 2(2): p. 127-134.
44. Braik, M., et al. A comparison between GAs and PSO in training ANN to model the TE chemical process reactor.  Proceedings of the AISB 2008 symposium on swarm intelligence algorithms and applications. p. 24-30.
45. Chopra, S., et al., Reduction of fuzzy rules and membership functions and its application to fuzzy PI and PD type controllers. International Journal of Control, Automation and Systems, 2006. 4(4): p. 438.
46. Ming-bao, P., et al. Traffic flow prediction of chaos time series by using subtractive clustering for fuzzy neural network modeling.  Intelligent Information Technology Application, 2008 IITA'08 Second International Symposium on: IEEE, 2008. p. 23-27.
47. Karimpouli, S., et al., Estimation of P-and S-wave impedances using Bayesian inversion and adaptive neuro-fuzzy inference system from a carbonate reservoir in Iran. Neural Computing and Applications, 2016. p. 1-14.
48. Fattahi, H., et al., Hybrid ANFIS with ant colony optimization algorithm for prediction of shear wave velocity from a carbonate reservoir in Iran. Int Journal of Mining & Geo-Engineering, 2016. 50(2): p. 231-238.
49. Fattahi, H., et al., Prediction of porosity and water saturation using pre-stack seismic attributes: a comparison of Bayesian inversion and computational intelligence methods. Computational Geosciences, 2016. 20(5): p. 1075-1094.
Journal of Analytical and Numerical Methods in Mining Engineering
Volume 6, Special Issue
January 2017
Pages 11-24

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History

  • Receive Date: 30 September 2015
  • Revise Date: 07 February 2016
  • Accept Date: 24 April 2017

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