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|Title:||Stochastic structural dynamic model updating and reliability prediction of a building using vibration data||Authors:||Chew, Xiao Yu||Keywords:||DRNTU::Engineering||Issue Date:||2014||Abstract:||Structural dynamics model updating can be defined as the adjustment of an existing analytical model using experimental data so that the model will more accurately reflect the dynamic behaviour of a structure of interest. Since model updating has a variety of applications in structural health monitoring, control and response prediction, it is also essential to consider all uncertainties and errors associated when constructing the predictive model. Thus the first objective of this report is to carry out model updating of dynamic structures so as to enhance the matching between experimental data and the corresponding model output. A Bayesian model updating approach based on Gibbs Sampler (GS) is adopted to provide a robust and rigorous framework for this application. To evaluate a system’s performance under dynamic excitations, all the uncertainties involved have to be performed using stochastic system analysis. Hence, evaluating the robust failure probability (complementary of robust reliability) of the system is a very important part of such stochastic system analysis. The word ‘failure’ used here refers to unsatisfactory performance of the system. System data will be used for the updating of robust failure probability that any particular response (e.g., inter-storey drift, floor acceleration) of a linear structural dynamic system exceeds a specified threshold during the time when the system is subjected to future Gaussian dynamic excitation. Thus the second objective of this report is to present a general framework for updating the reliability of a system using modal data. The effectiveness and efficiency of the proposed methodology will be illustrated using two examples: a 4-DOF mechanical system and the benchmark structure from Phase II of the IASC-ASCE benchmark problem in structural health monitoring.||URI:||http://hdl.handle.net/10356/60010||Rights:||Nanyang Technological University||Fulltext Permission:||restricted||Fulltext Availability:||With Fulltext|
|Appears in Collections:||CEE Student Reports (FYP/IA/PA/PI)|
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