Structural parameter evaluation using incomplete vibration measurement time series

Bin Xu

doi:10.1117/12.716611

10 April 2007 Structural parameter evaluation using incomplete vibration measurement time series

Bin Xu

Proceedings Volume 6529, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2007; 65292Q (2007) https://doi.org/10.1117/12.716611
Event: SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring, 2007, San Diego, California, United States

Abstract

It is difficult to obtain dynamic response measurement of a whole structure in reality, development of structural parametric identification methodologies using spatially incomplete dynamic response measurement is critical for performance evaluation and the realization of infrastructure sustainability. A general structural parametric identification methodology by the direct use of free vibration acceleration time histories without any eigenvalue extraction process that is required in many inverse analysis algorithms is proposed. An acceleration-based neural network (ANN) and a parametric evaluation neural network (PENN) are constructed to identify structural inter-storey stiffness and damping coefficients using an evaluation index called root mean square of prediction difference vector (RMSPDV). The performance of the proposed methodology using spatially incomplete acceleration measurements is examined by numerical simulations with a multi-degree-of-freedom (MDOF) shear structure involving all stiffness and damping coefficient values unknown. Numerical simulation results show that the proposed methodology is a practical method for near real-time identification and damage detection when several seconds of spatially incomplete dynamic responses measurements are available.

Citation Download Citation

Bin Xu "Structural parameter evaluation using incomplete vibration measurement time series", Proc. SPIE 6529, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2007, 65292Q (10 April 2007); https://doi.org/10.1117/12.716611

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