Conflict Fusion Analysis on Earthquake-Damaged Risk Evidences for Underground Structure

Abstract:

To account for the conflict sensitivity, one model is presented to fuse the high conflict risk evidences about earthquake-damaged underground structure. Following the nature ideology and model rule of Evidence Theory, the earthquake-damaged origin risk evidence is revised with Similarity Coefficients, and the identical intensity and conflict intensity is calculated for each risk evidence; the difference and conflict character is comparatively analyzed about the fusion rules respectively on Similarity Coefficient and Conflict Intensity; Under Standard DS Fusion Mode and Conflict Intensity Fusion Method, the four combination fusion model is presented as Model-AO, Model-RO, Model-AC and Model-RC, and the improved risk fusion operator is given for such earthquake-damaged underground structure evidences. Finally, case demonstrates the validity of the integrated model, which could overcome the high conflict lack in the risk fusion standard DS model.

• Periodical: Applied Mechanics and Materials (Volume 858)
• Pages: 163-170
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.858.163
• Online since: November 2016
• Authors: Feng Shan Wang*, Quan Bing Rong, Hong Jun Zhang
• Keywords: Conflict Intensity, Earthquake, Evidence Theory, Information Fusion, Similarity Coefficient, Underground Structure

References

[1] P. Zhang, W. Yan, Dynamic response analysis of underground rock tunnel under earthquake load, 2nd International Conference on Structures and Building Materials, 446-449 (2012) 2089-(2092).

DOI: 10.4028/scientific5/amr.446-449.2089

[2] Z. Z. Wang, Z. Zhang, Seismic damage classification and risk assessment of mountain tunnels with a validation for the 2008 Wenchuan earthquake, Int. J. Soil Dynam. Earthq. Eng. 45(2) (2012) 45-55.

DOI: 10.1016/j.soildyn.2012.11.002

[3] F. S. Wang, H. J. Zhang, W. H. Zhu, Damage information fusion model to military engineering based on intellectualized structure, 2010 International Conference on Computing, Control and Industrial Engineering, 333-336.

DOI: 10.1109/ccie.2010.91

[4] F. B. Yang, X. X. Wang, Conflict evidence combination method on D-S evidence theory, Beijing, National Defense Industry Press, (2010).

[5] H. Zhang, Y. J. Wang, D. Y. Luo, Modified D-S evidence theory and its application in water quality assessment, Chin. J. Comput. Syst. 31(6) (2010) 1236-1239.

[6] R. Yager, On the dempster-shafer framework and new combination rules, Int. J. Inform. Sci. 41(2) (1989) 93-137.

[7] P. Smets, The combination of evidence in the transfemble belief model, IEEE T. Patt. Analys. Mach. Intell. 12(5) (1990) 447-458.

[8] K. Pan, H. Li, G. Xing, Conflict evidence synthesis method based on confidence distance, Chin. J. Comput. Eng. 39(1) (2013) 290-293.

[9] C. K. Murphy, Combining belief functions when evidence conflicts, Int. J. Decis. Support Syst. 29(1) (2000) 1-9.

[10] Y. Li, Y. G. Wang, X. M. Xu, Research on convergence and conflict treatment in evidence fusion, Chin. J. Syst. Eng. Electron. 34(6) (2012) 1113-1119.

[11] L. Zadeh, A simple view of the dempster-shafer theory of evidence and its impficafion for the rule of combination, Int. J. AI Mag. 7(1) (1986) 85-90.

[12] R. Luo, M. G. Kay, Multisensor integration and fusion in intelligent systems, IEEE Trans. SMC, 19(5) (1989) 901-931.

DOI: 10.1109/21.44007

[13] H. Luo, Y. P. Yin, X. X. Hu, et al. Method to modify evidence source based on trustworthy factors, Chin. J. Syst. Eng. Electron. 37(6) (2015) 1459-1464.

[14] H. F. Wang, X. Y. Wang, Development and problems of D-S evidential reasoning in multisensor data fusion, Chin. J. Mod. Electron. Technol. 15 (2009) 7-9.

[15] L. W. Zhang, J. S. Yuan, Power transformer fault diagnosis based on typical sample and evidence theory, Chin. J. Electr. Measure. Instrument. 50(8) (2013) 14-19.

[16] Y. Liu, X. G. Gao, G. S. Lu, et al. Multisensor target recognition based on the weighted evidence combination, Chin. J. Syst. Eng. Electron. 25(12) (2003) 1475-1477.