Reliability updating of civil engineering dynamic systems using vibration data

• Main Author: Loh, Wei Jie
• Other Authors: Cheung Joseph Sai Hung
• Format: Final Year Project
• Language: English
• Published: 2015
• Subjects: DRNTU::Engineering::Civil engineering::Structures and design
• Online Access: http://hdl.handle.net/10356/63725
• Institution: Nanyang Technological University

In structural engineering, dynamic systems are required to fulfil both the structural strength and serviceability. Reliability is of great concern to be assessed when the system is subjected to excitation. The assessment is significant in both design and operation stage as the condition of the system may deteriorate due to dynamic loadings and material corrosion. This report will discuss the idea of updating the robust failure probability of a structure subjected to stochastic force through computing and comparing the modal data with the measured data. The calculation of the robust reliability will also take the model uncertainties into consideration as well as the random external forces. The Bayesian linear model updating approach will be capable to improve the degree of correlation between the output result from predictive model and the experimental data. It will be implemented together with the Gibbs sampler and Markov Chain Monte Carlo method to provide a reliable and rigorous framework for the model structure. Failure which refers to the unsatisfactory performance of a building can be defined in numerous criterions. The failure cases that would be discussed in this report will be defined in maximum displacement of the roof of building, maximum inter-storey displacement and the maximum absolute acceleration of the masses. The efficiency of the proposed method will be illustrated by applying to a 4-DOF shear building (linear elastic structural building model). In summary, the result of this study shows that structural models need to be updated by using numerous measured data collected from sensors in order to improve the robust reliability and to correlate better with the real structure condition. Also, it has shown that the results of updated robust reliability are sensitive with respect to different combination of sensor locations.