An objective of the structural health monitoring system is to identify the state of the structure and to detect its damages after a major event, such as the earthquake, to ensure the reliability and safety of structures. Innovative analysis techniques for the damage detection of structures have been extensively studied recently. However, practical and effective damage identification techniques remain to be developed for nonlinear structures, in particular nonlinear hysteretic reinforced concrete (RC) structures. In this paper, in addition to the equivalent time-varying linear model, a smooth hysteretic model with stiffness and strength degradations and with the pinching effect is used to represent the dynamic characteristics of reinforced concrete (RC) frames. A system identification technique capable of detecting damages in nonlinear structures, referred to as the adaptive quadratic sum-square error with unknown inputs (AQSSE-UI), is used to track the degradation of the time-varying parameters of nonlinear RC frames. The performance of the AQSSE-UI technique is also demonstrated by the experimental data. Six identical 1/2-scale one-story two-bay RC frames have been designed and tested on the shake table at NCREE, Taiwan. Each RC frame was subject to different levels of seismic excitations followed by cyclic loads until failure. Test data were used to verify the capability of the AQSSE-UI technique in detecting structural damages. Experimental results demonstrate that the AQSSE-UI technique is quite effective in tracking (i) the stiffness degradation of equivalent linear time-varying structure, and (ii) the non-linear hysteretic parameters with stiffness and strength degradations.

Date Published: 14 April 2011
PDF: 13 pages
Proc. SPIE 7981, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2011, 79812O (14 April 2011); doi: 10.1117/12.877203

Published in SPIE Proceedings Vol. 7981:
Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2011
Masayoshi Tomizuka, Editor(s)