Bayesian Network Based Expert System for Tunnel Surrounding Rockmass Classification

Abstract:

Traditional surrounding rockmass classification methods have disadvantages of relative narrow scope of application, most of the time the classification result needs some modifications by geological expert and field situation. Based on the surrounding rockmass classification methods of BQ system, the Bayesian network and corresponding uncertainty reasoning principle has been introduced to develop an expert system for surrounding rockmass classification. By combining prior knowledge of domain experts with worksite data recorder, we get the posterior probability density of most nodes. The field practices proved that the expert system has good applicability.

• Periodical: Applied Mechanics and Materials (Volume 482)
• Pages: 248-251
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.482.248
• Online since: December 2013
• Authors: Hong Tao Xie*
• Keywords: Bayesian Network (BN), Expert System, Surrounding Rockmass Classification, Tunnel Engineering

References

[1] GE Hua, WANG Guang-de, SHI Yu-chuan, KOU Jia-wei, SHAN Zhi-gang, ZHOU Chun-hong. The applicability of the common surrounding rockmass classification methods in a deep buried tunnel[J]. The Chinese Journal of Geological Hazard and Control. 2006, 17(2): 44-49, in chinese.

[2] WANG Ying-chao, SUN Hong-yue, SHANG Yue-quan, ZHU Mo. Classification of surrounding rock based on Delphi method and ideal point method[J]. Chinese Journal of Geotechnical Engineering. 2010, 32(4): 651-656, in chinese.

[3] ZHAO Hong-bo, FENG Xia-ting, YIN Shun-de. Classification of engineering rock based on support vector machine[J]. Rock and Soil Mechanics, 2002, 23(6): 698–701, in chinese.

[4] YUAN Guo-hong, CHEN Jian-ping, MA Lin. APPLICATION OF EXTENICS IN EVALUATING OF ENGINEERING QUALITY OF ROCK MASSES[J]. Chinese Journal of Rock Mechanics and Engineering, 2005, 24(9): 1539–1544, in chinese.

[5] GONG Feng-qiang, LI Xi-bing, GAO Ke. Catastrophe progression method for stability classification of underground engineering surrounding rock[J]. Journal of Central South University(Science and Technology), 2008, 39(5): 1081- 1086, in chinese.

[6] YU De-hai, PENG Jian-bing. Visual evaluation of neural network on classification of surrounding rocks for underground engineering[J]. The Chinese Journal of Geological Hazard and Control, 2005, 16(4): 116–119 , in chinese.

[7] Eleye-Datubo AG, Wall A, Saajedi A, Wang J. Enabling a powerful marine and offshore decision-support solution through Bayesian network technique. Risk Analysis 2006; 26(3): 695–721.

DOI: 10.1111/j.1539-6924.2006.00775.x

[8] Mehmet Emre Bayraktar, Makarand Hastak. Bayesian Belief Network Model for Decision Making in Highway Maintenance: Case Studies[J], Journal of Construction Engineeringand Management, 2009, 135(12): 1357-1369.

DOI: 10.1061/(asce)co.1943-7862.0000111

[9] Percy DF, Kobbacy AH. Preventative maintenance modelling, a Bayesian perspective. Journal of Quality in Maintenance Engineering 1996; 2(1): 15–24.

DOI: 10.1108/13552519610113818

[10] Ben-Gal, I. (2007). Bayesian networks. In F. Ruggeri, R. S. Kennett, & F. W. Faltin(Eds. ). Encyclopedia of statistics in quality and reliability (Vol. 1). John Wiley & Sons.