Model and response updating of stochastic nonlinear dynamic systems using strong vibration data
Date of Issue2016
School of Civil and Environmental Engineering
There has long been interests to improve the behavior of structure under strong vibration generated by earthquake. As deriving the relevant character of structures through physics experiment will be costly, great attention is drawn to develop algorithm to improve the predictive capability of structural model mathematically based on historical data. Bayesian statistical framework is one of the guiding idea that current research focus on. In this approach, the probability of all models are treated as a set of candidate, and they are selected based on their possibility of appearance. Based on the idea of Bayesian updating, numbers mathematical algorism and its application have been developed. Among all these methods, Transitional Markov Chain Monte Carlo (TMCMC) method is one of the techniques that could solve the non-normalized posterior PDF problems. Therefore, this research focus on the application of TMCMC method to examine the feasibility of the technique. Two cases with different number of parameters are examined by applying TMCMC method. And it is found that the method works well on the case of less number of parameters. However, the algorithm may not be suitable to apply on the case with large number of parameters as the time needed to complete the calculation would be too long.
Final Year Project (FYP)
Nanyang Technological University