Making Sense of Non-Destructive Evaluation Data with an Artificial Intelligence Approach


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This paper presents the feasibility of an artificial intelligence technique for processing and interpretation of non-destructive evaluation (NDE) data from assessments of engineering structures. The technique used is a learning and reasoning approach with belief network. With this technique, causal factors and consequent indicators in the data structure in relation with structure/material condition can be modelled, and their causal relationship can be established using the NDE data as the learning resource. Fundamentals of the technique are briefly presented, and then the potential applications of the technique to NDE data are demonstrated in two case studies.



Key Engineering Materials (Volumes 321-323)

Edited by:

Seung-Seok Lee, Joon Hyun Lee, Ik Keun Park, Sung-Jin Song, Man Yong Choi






M. Nguyen et al., "Making Sense of Non-Destructive Evaluation Data with an Artificial Intelligence Approach", Key Engineering Materials, Vols. 321-323, pp. 294-297, 2006

Online since:

October 2006




[1] Neapolitan, R.E. (2004). Learning Bayesian Networks, Prentice Hall Press: USA.

[2] Netica, Norsys Software Corporation, http: /www. norsys. com.

[3] Su, Z. and L. Ye. 2002. A damage identification technique for CF/EP composite laminates using distributed piezoelectric transducers, Composite Structures, 57: 465-471.

DOI: 10.1016/s0263-8223(02)00115-0

[4] Nguyen, M., Wang, X., Foliente, G., Su, Z. and Ye, L. (2004). Damage identification for composite structures with a Bayesian network, Int. conf. on Sensors, Sensor Networks and Information Processing, Melbourne, Australia, Dec (2004).

DOI: 10.1109/issnip.2004.1417480

[5] Wang, X., Nguyen, M., Foliente, G, and Lowe, M. (2005). Embedding Learning and Reasoning into Structural Performance Assessment. Australian Structural Engineering Conference - ASEC 2005, Newcastle, Australia, Sep (2005).

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