Special Session S1 - EASEC-14

Special Session S1: Probabilistic system identification and health monitoring of structures
Chair: T. Yin, Wuhan University, China
Co-chair: H.F. Lam, City University of Hong Kong, China
The quality of randomness is inherent characteristic both of loads borne by structures and of the
properties of the structure themselves. For better description and assessment of structural behavior
and health status during the service period, probabilistic methods must be restored to, considering
all aspects of uncertainty arisen from external loading, structural properties etc.
This special session provides a forum for recent scholarly work dealing primarily with probabilistic
and statistical approaches to problems of structural modal and model parameters identification,
structural health monitoring and so on, encountered in diverse technical disciplines, such as
aerospace, civil, mechanical and nuclear engineering. The special session aims to maintain a
healthy balance between general computational techniques and problem-specific applied results,
encouraging a fruitful exchange of ideas among disparate engineering specialties within the scope
of probabilistic based health monitoring and system identification of structures.
Paper Info
C.T. Ng, S.K. Au
Probabilistic analysis of uncertainty in input-output and output-only modal
The University of Adelaide, Australia
H.F. Lam, J. Hu, M.C. Chan
Damage detection of a standing seam metal roofing system utilizing
ambient vibration data
City University of Hong Kong, Hong Kong
H.F. Lam, J.H. Yang
Application of MCMC-based Bayesian Model Updating for Structural
Health Monitoring
City University of Hong Kong, Hong Kong
H.S. Li, D.B. Wen, Y. Wang
Identifying the probabilistic distribution of fatigue crack growth using
maximum entropy principle
Nanjing University of Aeronautics and Astronautics, China
X.D. Xie, S.C. Zhang, L. Xiao, W.Z. Qu
Bolted looseness damage identification and localization using PZT array
based on NI system
Wuhan University, China
F.L. Zhang
Bayesian model updating using ambient vibration data from multiple
Tongji University, China
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