Data mining for tunnel support stability: Neural network approach

Automation in construction

Volumn 10, Issue 4, 2001, Pages 429-441

  Leu, Sou Sen ^a   Chen, Chee Nan ^a   Chang, Shiu Lin ^a  

^a NATIONAL TAIWAN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Taiwan)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER SIMULATION; DATA MINING; GROUND SUPPORTS; NEURAL NETWORKS; REGRESSION ANALYSIS; ROCK MECHANICS; SEDIMENTARY ROCKS; SLOPE STABILITY;

TUNNEL SUPPORT STABILITY;

RAILROAD TUNNELS;

EID: 0034836751     PISSN: 09265805     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0926-5805(00)00078-9     Document Type: Article

References (37)

1. H. Adeli, M.Y. Wu, Regularization neural network for construction cost estimation, J. Constr. Eng. Manage., Am. Soc. Civ. Eng. 124 (1) (1998) 18-24.
2. T. Arciszewski, W. Ziarko, Machine learning in knowledge acquisition, in: T. Arciszewski, L.A. Rossman (Eds.), Knowledge Acquisition in Civil Engineering, ASCE, New York, 1992.
3. N. Barton, R. Lien, J. Lunde, Engineering classification of rock masses for the design of tunnel support, Rock Mech. 6 (1974) 183-236.
4. Z.T. Bieniawski, The Geomechanic Classification in Rock Engineering Application, Proceedings of 4th International Congress of Rock Mechanics, Montreux, 1976, pp. 55-95.
5. Z.T. Bieniawski, Engineering Rock Mass Classifications, Wiley, New York, 1989.
6. B.H.G. Brady, E.T. Brown, Rock Mechanics for Underground Mining, 2nd edn., Chapman & Hall, London, 1993.
7. E.T. Brown (Ed.), Rock Characterization Testing and Monitoring, ISRM Suggested Methods, Pergamon, Oxford, 1981.
8. E.T. Brown, Putting the NATM in perspective, Tunnels Tunneling 13 (11) (1981) 13-17.
9. P. Cabena et al., Discovering Data Mining: from Concept to Implementation, Prentice-Hall, NJ, 1998.
10. J. Cattan, J. Mohammadi, Analysis of bridge condition rating data using neural networks, Microcomput. Civ. Eng. 12 (1997) 419-429.
11. L.C. Chao, M.J. Skibniewski, Estimating construction productivity, neural-network-based approach, J. Comput. Civ. Eng., Am. Soc. Civ. Eng, 8 (2) (1994) 234-251.
12. A.P. Deane, Application of NATM to design of underground station in London clay, Proceedings of 7th International Symposium of Tunneling, Chapman & Hall, London, 1994, pp. 87-97.
13. I. Flood, N. Kartam, Neural networks in civil engineering: I. Principles and understanding, J. Comput. Civ. Eng., Am. Soc. Civ. Eng. 8 (2) (1994) 131-148.
14. T. Froese, Models of construction process information, J. Comput. Civ. Eng., Am. Soc. Civ. Eng. 10 (3) (1996) 183-193.
15. A.T.C. Goh, Back-propagation neural network for modeling complex systems, Artif. Intell. Eng. 9 (1995) 143-151.
16. E. Grimstad, N. Barton, Updating of the Q system for NMT,Proceedings of International Symposium on Sprayed Concrete, Fagernes, Norway, 1993, pp. 17-21.
17. J. Hertz, Introduction to the Computation of Neural Computation, Addison-Wesley, Reading, MA, 1991.
18. R.A. Jacobs, Increasing rates for convergence through learning rate adaptation, Neural Networks 1 (1988) 295-307.
19. R.A. Jacobs et al., Adaptive mixture of local experts, Neural Comput. 3 (1991) 79-87.
20. N. Karunanithi et al., Neural network for river flow prediction, J. Comput. Civ. Eng., Am. Soc. Civ. Eng. 8 (2) (1994) 201-219.
21. N. Kartam, I. Flood, J.H. Garrett Jr. (Eds.), Artificial Neural Networks for Civil Engineers: Fundamentals and Applications, ASCE, New York, 1997.
22. J. Lawrence, Introduction to Neural Networks, 3rd edn., California Scientific Software, Grass Valley, CA, 1991.
23. J.A. Leonard, M.A. Kramer, L.H. Ungar, Using radial basis functions to approximate a function and its error bounds, IEEE Trans. Neural Networks 3 (4) (1992) 624-627.
24. R.S. Michalski, I. Bratko, M. Kubat, Machine Learning and Data Mining: Methods and Applications, Wiley, New York, 1998.
25. A.A. Minai, R.D. Williams, Acceleration of back propagation through learning rate and momentum rate adaptation, Int. Jt. Conf. Neural Networks 1 (1990) 676-679.
26. L. Muller, Removing misconceptions on the New Austrian Tunneling Method, Tunnels Tunneling 10 (1978) 29-32.
27. S. Pezeshk, C.V. Camp, S. Karprapu, Geophysical log interpretation using neural network, J. Comput. Civ. Eng., Am. Soc. Civ. Eng. 10 (2) (1996) 136-142.
28. Y. Reich, Machine learning techniques for civil engineering problems, Microcomput. Civ. Eng. 12 (1997) 295-310.
29. J.L. Roger, Simulating structural analysis with neural networks, J. Comput. Civ. Eng., Am. Soc. Civ. Eng. 8 (2) (1994) 252-265.
30. D. Rumelhart, J. McClelland, Parallel Distribution Processing: Explorations in the Miscrostructure of Cognition vol. 1-2 MIT Press, Cambridge, MA, 1986.
31. S. Sakurai, The Assessment of Tunnel Stability on the Basis of Field Measurements, 18th Convegno Nazionale Di Geotechnica, Rimini, Italy, 1993, pp. 1-10.
32. T. Sayed, W. Abdelwahab, Comparison of fuzzy and neural classifiers for road accident analysis, J. Comput. Civ. Eng., Am. Soc. Civ. Eng. 12 (1) (1998) 42-47.
33. A.L. Stumpf et al., Object-oriented model for integrating construction product and process information, J. Comput. Civ. Eng., Am. Soc. Civ. Eng. 10 (3) (1996) 204-212.
34. C.I. Teh et al., Prediction of pile capacity using neural network, J. Comput. Civ. Eng., Am. Soc. Civ. Eng. 11 (2) (1997) 129-138.
35. G.E. Wickham, H.R. Tiedemann, E.H. Skinner, Ground Support Prediction Model - RSR Concept, Proceedings Rapid Excavation and Tunneling Conference, AIME, New York, 1974, pp. 691-707.
36. R.L. Wilson, R. Sharda, Bankruptcy prediction using neural network, Decis. Support Syst. 11 (1994) 545-557.
37. J.M. Zurada, Introduction to Artificial Neural Systems, West Publishing, St. Paul, 1992.