Predicting the most important geomechanical parameter of rock mass using the harmony search and teaching learning based optimization algorithms

مهندسی عمران فردوسی

Article 7, Volume 35, Issue 3 - Serial Number 39, December 2022, Pages 1-18   

Document Type: Original Article

DOI: 10.22067/jfcei.2022.72095.1056

Authors

Hadi Fattahi^{* 1}; Farshad Malekmahmoudi²; Hossein Ghaedi²

¹Faculty of Earth Sciences Engineering, Arak University of Technology, Iran

²Faculty of Earth Sciences Engineering, Arak University of Technology, Arak, Iran.

Abstract

Due to the difficulties in assessing the deformation of jointed aggregates at the laboratory scale, various in situ testing methods such as plate loading test and dilatometry can be used to consider the effect of scale and joints. Although these methods are currently the best, they are expensive, time consuming, and have operational difficulties during implementation. Therefore, in this paper, to overcome these problems, new harmony search algorithms (HS) and teaching-learning optimization algorithm (TLBO) are used to indirectly estimate the modulus of rock mass deformation. In these models, the rock mass classification score (RMR), uniaxial compressive strength of virgin rock (UCS), depth (D) and the modulus of elasticity of intact rock (Ei) as input parameters and the modulus of rock mass deformability (Em) as output parameter Used. In this paper, Using different statistical indicators, the model created by the algorithms is evaluated and validated. The evaluation results showed that the relationship accuracy for the harmonic search algorithm using R2 and VAF methods is about 0.91-0.93 and using the RMSE and MSE methods is between 0.000017-0.0042. Also, the relationship accuracy for the optimization algorithm Based on teaching and learning using R2 and VAF methods, about 0.92-0.95 and using RMSE and MSE methods were between 0.00001- 0.0032.

Keywords

Deformation of modulus; harmony search algorithm; teaching-learning optimization algorithm; rock mass

Supplementary Files

1.pdf

References

1. Bieniawski, Z., "Engineering classification of jointed rock masses". Civil Engineer in South Africa, Vol. 15, No. 12, (1973).
2. Serafim, J. Consideration of the geomechanical classification of Bieniawski. in Proc. int. symp. on engineering geology and underground construction. (1983).
3. Verman, M., et al., "Effect of tunnel depth on modulus of deformation of rock mass". Rock mechanics and rock engineering, Vol. 30, No. 3, pp. 121-127, (1997).
4. Nicholson, G. and Bieniawski, Z., "A nonlinear deformation modulus based on rock mass classification". International journal of Mining and geological engineering, Vol. 8, No. 3, pp. 181-202, (1990).
5. Mehrotra, V., "Estimation of engineering parameters of rock mass". University of Roorkee, Vol., (1992).
6. Diederichs, M. and Kaiser, P., "Stability of large excavations in laminated hard rock masses: the voussoir analogue revisited". International Journal of Rock Mechanics and Mining Sciences, Vol. 36, No. 1, pp. 97-117, (1999).
7. Read, S., Perrin, N., and Richards, L. Applicability of the Hoek-Brown failure criterion to New Zealand greywacke rocks. in 9th ISRM Congress. International Society for Rock Mechanics and Rock Engineering. (1999).
8. Kim, G.-W. Revaluation of geomechanics classifications of rock masses. in Proceedings of the Korean geotechnical society of spring national conference, Seoul. (1993).
9. Mitri, H., Edrissi, R., and Henning, J., "Finite-element modeling of cable-bolted stopes in hard-rock underground mines". Transactions-Society For Mining Metallurgy And Exploration Incorporated, Vol. 298, No. pp. 1897-1902, (1995).
10. Grimstad, E. Updating the Q-system for NMT. in Proceedings of the International Symposium on Sprayed Concrete-Modern use of wet mix sprayed concrete for underground support, Fagemes, Oslo, Norwegian Concrete Association, (1993).
11. Barton, N., "Some new Q-value correlations to assist in site characterisation and tunnel design". International journal of rock mechanics and mining sciences, Vol. 39, No. 2, pp. 185-216, (2002).
12. Hoek, E. and Brown, E.T., "Practical estimates of rock mass strength". International journal of rock mechanics and mining sciences, Vol. 34, No. 8, pp. 1165-1186, (1997).
13. Hoek, E., Carranza-Torres, C., and Corkum, B., "Hoek-Brown failure criterion-2002 edition". Proceedings of NARMS-Tac, Vol. 1, No. 1, pp. 267-273, (2002).
14. Hoek, E. and Diederichs, M.S., "Empirical estimation of rock mass modulus". International journal of rock mechanics and mining sciences, Vol. 43, No. 2, pp. 203-215, (2006).
15. Zhang, L. and Einstein, H., "Using RQD to estimate the deformation modulus of rock masses". International Journal of Rock Mechanics and Mining Sciences, Vol. 41, No. 2, pp. 337-341, (2004).
16. Deere, D.U., Coon, R.F., and Merritt, A.H., Engineering classification of in-situ rock. 1969, Illinois Univ at Urbana  Dept of Civil Engineering.
17. Jose, M., Galeral, M., and Alvarez, T., "Bieniawski evalution of the deformation modulus of rock masses: comparison of pressuremeter and dilatometer tests with RMR prediction". 1SP5-PRESSIO International Symposium, Vol., (2005).
18. Sonmez, H., Gokceoglu, C., and Ulusay, R., "Indirect determination of the modulus of deformation of rock masses based on the GSI system". International journal of rock mechanics and mining sciences, Vol. 41, No. 5, pp. 849-857, (2004).
19. Gardner, W.S. Design of drilled piers in the Atlantic Piedmont. in Foundations and excavations in decomposed rock of the Piedmont Province. ASCE. (1987).
20. Zhang, L. and Einstein, H., "Using RQD to estimate the deformation modulus of rock masses". International journal of rock mechanics and mining sciences (1997), Vol. 41, No. 2, pp. 337-341, (2004).
21. Palmström, A. and Singh, R., "The deformation modulus of rock masses—comparisons between in situ tests and indirect estimates". Tunnelling and Underground Space Technology, Vol. 16, No. 2, pp. 115-131, (2001).
22. Gokceoglu, C., Sonmez, H., and Kayabasi, A., "Predicting the deformation moduli of rock masses". International Journal of Rock Mechanics and Mining Sciences, Vol. 40, No. 5, pp. 701-710, (2003).
23. Kayabasi, A., Gokceoglu, C., and Ercanoglu, M., "Estimating the deformation modulus of rock masses: a comparative study". International Journal of Rock Mechanics and Mining Sciences, Vol. 40, No. 1, pp. 55-63, (2003).
24. Galera, M., Alvarez, M., and Bienawski, Z., "Evaluation of the deformation modulus of rock masses. Comparison by pressuremeter and dilatometer test". ISP5–PRESSIO 2005, Vol. 2, No. pp. 239-256, (2005).
25. Chun, B.-S., et al., "Indirect estimation of the rock deformation modulus based on polynomial and multiple regression analyses of the RMR system". International Journal of Rock Mechanics and Mining Sciences, Vol. 46, No. 3, pp. 649-658, (2009).
26. Nejati, H.R., et al., "On the use of the RMR system for estimation of rock mass deformation modulus". Bulletin of engineering geology and the environment, Vol. 73, No. 2, pp. 531-540, (2014).
27. Geem, Z.W., Kim, J.H., and Loganathan, G.V., "A new heuristic optimization algorithm: harmony search". simulation, Vol. 76, No. 2, pp. 60-68, (2001).
28. Lee, K.S. and Geem, Z.W., "A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice". Computer methods in applied mechanics and engineering, Vol. 194, No. 36-38, pp. 3902-3933, (2005).
29. Geem, Z.W., Global optimization using harmony search: Theoretical foundations and applications, in Foundations of Computational Intelligence Volume 3. 2009, Springer. p. 57-73.
30. Moh’d Alia, O. and Mandava, R., "The variants of the harmony search algorithm: an overview". Artificial Intelligence Review, Vol. 36, No. 1, pp. 49-68, (2011).
31. Yuan, X., et al., "Hybrid parallel chaos optimization algorithm with harmony search algorithm". Applied Soft Computing, Vol. 17, No. pp. 12-22, (2014).
32. Jaberipour, M. and Khorram, E., "Two improved harmony search algorithms for solving engineering optimization problems". Communications in Nonlinear Science and Numerical Simulation, Vol. 15, No. 11, pp. 3316-3331, (2010).
33. Rao, R.V., Savsani, V.J., and Vakharia, D., "Teaching–learning-based optimization: an optimization method for continuous non-linear large scale problems". Information sciences, Vol. 183, No. 1, pp. 1-15, (2012).