sions and compare with the deduced decision parts rules. 

| Rule 1.                                | (cb<=220000.000000) => (mvv at   |
|----------------------------------------|----------------------------------|
| most 1);  Rule 2. (phib<=25.035000)    | => (mvv at                       |
| most 1);  Rule 3. (csz<=999.790000)    | => (mvv at                       |
| most 1);  Rule 4. (phisz<=5.035400)    | &                                |
| (tb<=42844.000000) => (mvv at most 1); |                                  |

Figure 7. Part of the extracted rules by the proposed algorithm 

## 5 Conclusions

The role of uncertainty and vague information in geomechnaic analysis is undeniable feature. Indeed, with developing of new approaches in information theory and computational intelligence, as well as soft computing approaches, it is necessary to consider these approaches within and inside of current and conventional analysis, especially in geomechanic field. Under this idea and to fining best information granules which are picked off inside each other, close-open worlds (cycle) procedure has been proposed. Utilizing this algorithm in interaction with finite difference method and inferring back analysis results are main advantages of our method. So, we could obtain a becoming approximation of the parameters (in failure of southeast wall of Jeffry mine -1971). It must be notice that application of such process ensues a (or some) reduct set of the attributes where the reduct set(s) can be supposed as soft sensitive analysis on the model. Decreasing of time consuming and extractions of effective parameters, behind the core(s)-most effective parameters- of attributes (in condition parts) are other benefits of our model. 

## References

Cividini, A.Jurino, L.Gioda, G.1989.Some aspects of characterization problems in geomechanics, Int J Rock Mech Min Sci. 

18:487-503. 

Feng,X.T.Zhao,H,Li,S. 2004. A new displacement back analysis to identify mechanical geomaterial parameters based on hybrid intelligent methodology. Int J Numer Anal Meth Geomch 28:1141-1165. 
Greco, S. Matarazzo, B. Slowinski, R. 2001. Rough sets theory for multi criteria decision analysis, European Journal of Operational Research 129: 1-47. 

Kohonen, T. 1987. Self-organization and associate memory. 

2nd ed. Springer - Verlag. Berlin . 

Pal, K.S. Polkowski, LA. Skowron, A. 2004. Rough-neural computing: techniques for computing with words. springer-verlag. 

Pawlak, Z. 1982. Rough sets. Int J Comput Inform Sci 11: 
341-356. 
Pawlak, Z. 1991. Rough sets: theoretical aspects reasoning about data. Kluwer academic. Boston. 

Pichler,B.Lackner,R. Mang, H.A.2003.Back analysis of model parameters in geotechnical engineering by means of soft computing. Int J Numer Anal Meth Geomch 57:19431978. 
Sakurai,S.Akutagawa,S. Takeuchi, Shimiziu,N. 2003. Back analysis for tunnel engineering as a modern observational method. Tunneling &underground space technology. 18(23): 185-196. 

Shahriar,k. Owladeghaffari, H. 2007.Permeality analysis using BPF,SOM&ANFIS. 1st Canada-U.S. Rock Mechanics Symposium (in print). 

Sirat, M. Talbot, C. J. 2001. Application of artificial neural networks to fracture analysis at the Aspo HRL, Sweden:fracture sets classification. Int J Rock Mech Min Sci. 

38: 621-639. 

Sjoberg,J. 1996.Technical report: large scale slope stability in open pit mining . LULEA University. ISSN 0349-3571. 

Zadeh, L.A. 1997. Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic . Fuzzy sets and systems 90: 111-127. 

Zane, L.A.1994.Fuzzy logic, neural networks, and soft computing. Commun ACM vol. 37 77–84. Zhu, W. Zhao, J. 2004. Stability analysis and modeling of underground excavation in fractured rocks. Elsevier. 