Identification of Crack Locations and Extents by Bayesian Model Class Selection
This paper puts forward a method for the detection of crack locations and extents on a structural member utilizing measured dynamic responses following the Bayesian probabilistic framework.In the proposed crack detection method a beam with different number of cracks is modeled using different classes of models.The Bayesian model class selection method is then applied to select the “most plausible class of models in order to identify the number of cracks on the structural member.The objective of the proposed method is not to pinpoint the crack locations and extents but to calculate the posterior(updated)probability density function(PDF)of crack parameters(i.e.,crack locations and extents).The method explicitly handles the uncertainties introduced by measurement noise and modeling error.This paper presents not only the theoretical development of the proposed method but also the numerical and experimental verifications.In the numerical case studies,noisy data generated by a Bernoulli-Euler beam with semi-rigid connections is used to demonstrate the procedures of the proposed method.The method is finally verified by measured dynamic responses of a cantilever beam utilizing laser Doppler vibrometer.
Multiple cracks detection Bayesian model class selection Transient vibration Laser Doppler vibrometers
H.F. Lam C.T. Ng L.S. Katafygiotis
Department of Building and Construction,City University of Hong Kong,Hong Kong,China Department of Civil Engineering,Hong Kong University of Science and Technology,Hong Kong,China
国际会议
The World Forum on Smart Materials and Smart Structures Technology(SMSST07)(2007年世界智能材料与智能结构技术论坛)
重庆·南京
英文
2007-05-01（万方平台首次上网日期，不代表论文的发表时间）