Experimental Vibration Analysis for Civil Structures

Testing, Sensing, Monitoring, and Control
Herausgegeben:Conte, Joel P.; Astroza, Rodrigo; Benzoni, Gianmario; Feltrin, Glauco; Loh, Kenneth J.; Moaveni, Babak

• Broschiertes Buch

Produktbeschreibung

This edited volume presents selected contributions from the International Conference on Experimental Vibration Analysis of Civil Engineering Structures held in San Diego, California in 2017 (EVACES2017). The event brought together engineers, scientists, researchers, and practitioners, providing a forum for discussing and disseminating the latest developments and achievements in all major aspects of dynamic testing for civil engineering structures, including instrumentation, sources of excitation, data analysis, system identification, monitoring and condition assessment, in-situ and laboratory experiments, codes and standards, and vibration mitigation.

Produktdetails

• Lecture Notes in Civil Engineering 5
• Verlag: Springer / Springer International Publishing / Springer, Berlin
• Artikelnr. des Verlages: 978-3-319-88430-1
• Softcover reprint of the original 1st ed. 2018
• Seitenzahl: 936
• Erscheinungstermin: 18. Mai 2018
• Englisch
• Abmessung: 235mm x 155mm x 50mm
• Gewicht: 1388g
• ISBN-13: 9783319884301
• ISBN-10: 3319884301
• Artikelnr.: 55130141

Inhaltsangabe

Experimental evaluation of the effect of geometric nonlinearities on structural resonances.- Vortex-induced vibration of stay cables, verification on the footbridge.- The selection of a dynamic model of a RC chimney based on in situ research.- A new approach to damage detection in bridges using machine learning.- Vibration based damage identification method for cantilever beam using artificial neural network.- Vertical vibration mitigation of a bridge cable using a non smooth absorber under gravity.- Advanced statistical techniques applied to raw data for structural damage detection.- A new system identification method operated in the pole domain.- Identification of extremely close modes with an improved stochastic subspace algorithm.