A Case Library Creating Method Based on Risk Control of Tunnel Engineering

Zi Qi Tang; Hui Ming Wu; Yi Wen Bian

doi:10.4028/www.scientific.net/AMR.323.151

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HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 323A Case Library Creating Method Based on Risk...

A Case Library Creating Method Based on Risk Control of Tunnel Engineering

Abstract:

Because of low knowledge structural level in real tunnel engineering management, existing risk control methods (e.g., rule reasoning, decision tree inference) cannot completely meet the actual engineering demands. Considering the complexity of risk analysis of the tunnel engineering, this paper presents an aggregated risk control approach for tunnel engineering based on case library, i.e., representing case attribute modules using Frame representation method, reducing attributes using rough set technology, and simplifying case library by removing its redundancy in attribute similarity threshold.

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Periodical:

Advanced Materials Research (Volume 323)

Pages:

151-156

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.323.151

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Online since:

August 2011

Authors:

Zi Qi Tang, Hui Ming Wu, Yi Wen Bian

Keywords:

Case-Based Reasoning (CBR), Risk Control of Tunnel Engineering, Rough Set Theory (RST)

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References

[1] Einstein H H, Vick C.S. Geological model for tunnel cost model. Procapid Excavation and Tunneling Conf, 2nd, 1974: 170-172.

Google Scholar

[2] Broeren PTW. BOMVIT Beslissings. ondersteunend model. Vluchtstroken In tunnels, Case: Leidsche Rijn, Concept 23-10-2002;2003.

Google Scholar

[3] Ian Watson & Farhi Marir, Case-Based Reasoning: A Review, Dept. of Computer Science, University of Auckland, New Zealand, (1994)

Google Scholar

[4] Jiawei Han, Micheline Kamber. Data Minig: Concepts and Techniques. USA: Morgan Kaufmann Publishers, (2001)

Google Scholar

[5] Tsumoto Shetal.Extraction of Domain Knowledge from Databases Based on Rough Set Theory. IEEE International Conference on Fuzzy Systems, New Jersey, 1996, 748-754

DOI: 10.1109/fuzzy.1996.552274

Google Scholar

[6] Zdzislaw Pawlak. Rough Sets: Theoretical Aspects of Reasoning about Data. Dortrecht: Kluwer Academic Publishers, (1991)

Google Scholar

[7] Zdzislaw Pawlak. Rough Sets and Decision Algorithms. RSCTC 2000, LNAI2005, 2001: 30-45

Google Scholar

[8] Zdzislaw Pawlak. Rough Sets. International Journal of Computer and Information Sciences, 1982, 11(5): 341-356

Google Scholar