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Computational Science, Engineering & Technology Series
ISSN 1759-3158
Edited by: Y. Tsompanakis, P. Iványi and B.H.V. Topping
Chapter 8

Soft Computing Applications in Structural Dynamic Monitoring

G. Quaranta1 and G.C. Marano2

1Department of Structural and Geotechnical Engineering, Sapienza University of Rome, Italy
2Department of Civil Engineering and Architecture, Technical University of Bari, Italy

Full Bibliographic Reference for this chapter
G. Quaranta, G.C. Marano, "Soft Computing Applications in Structural Dynamic Monitoring", in Y. Tsompanakis, P. Iványi and B.H.V. Topping, (Editors), "Civil and Structural Engineering Computational Methods", Saxe-Coburg Publications, Stirlingshire, UK, Chapter 8, pp 157-170, 2013. doi:10.4203/csets.32.8
Keywords: differential evolution, genetic algorithm, health monitoring, optimal sensor placement, parametric identification, particle swarm optimization, seismic protection.

Soft computing based tools and methodologies are attracting growing interest in the field of structural dynamic monitoring. Within this framework, neural networks, evolutionary computation,metaheuristic and swarm intelligence are becoming very popular in sensor network design, signal processing, system identification, model updating and structural diagnostic. Current research also shows increasing use of fuzzy logic for damage detection and structural diagnostic. The paper provides a short state-of-the-art review about the most recent research on soft computing theories and techniques for structural dynamic monitoring, with the focus on optimal sensor placement, mechanical system identification and health monitoring. Finally, some experimental applications are included to highlight how soft computing methods can be employed effectively in this field. They are concerned with the experimental parametric identification of nonlinear passive devices for seismic protection using differential evolution and particle swarm optimization.

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