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advanced analysis of steel frames by Wai-Fah Chen, Shouji Toma

By Wai-Fah Chen, Shouji Toma

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4–111. , Boe, P. D. -C. (2005). Structural damage diagnosis under varying environmental conditions part ii: local pca for non-linear cases, Mechanical Systems and Signal Processing 19, pp. 865–880. , Gonza´ alez, M. and Worden, K. (2003). Damage assessment using neural networks, Mechanical Systems and Signal Processing 17, pp. 119–125. 5in Chapter 2 Enhanced Damage Locating Vector Method for Structural Health Monitoring S. T. Quek1, V. A. Tran2, and N. N. K. sg 2 1. 1. General concept Consider a linear elastic structure with ns sensors attached.

Foote, P. (2011). The aerospace industry steering committee on structual health monitoring management (aisc shm): Progress on shm guidelines for aerospace, in Proceedings of the 8th International Workshop on Structural Health Monitoring. Friswell, M. and Mottershead, J. (1995). Finite element model updating in structural dynamics, Vol. 38 (Springer). -P. and Bohle, K. (2003). Global damage identification of the ’steelquake’ structure using modal data, Mechanical Systems and Signal Processing 17, pp.

In the civil engineering context, concrete is an extremely complex material and is almost always understood to have inherent flaws which are dealt with in the design process through the careful use of highly evolved codes. Axiom III provides some interesting points for discussion in the context of civil infrastructure. Because only the lowest global modes are generally excited in practice for say a bridge, the modes will usually involve large scale coherent motions. If the structure has a dense sensor network, a breakdown in correlations between neighbouring sensors may give information about the location of damage.

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