Collaborative Research: Efficient Probabilistic Approach Using Order Reduction and Hybrid Models -- A New Paradigm for Structural Dynamic Analysis

University Of Connecticut, Storrs CT

Investigators

Abstract

The objective of this collaborative research between University of Connecticut and Texas A&M University is to develop a new probabilistic framework of dynamic analysis for handling large-scale structures under uncertainties. In particular, the research team aims at tackling a number of research challenges such as high computational cost and difficulty in characterizing model uncertainties, often encountered in this type of analysis. The approach in this research is built upon a hybrid modeling and analysis strategy, using both the computations and measurements of structural modal responses. It features the combination of structural dynamic model order-reduction, Bayesian response prediction (based on a set of viable candidate models), and recursive model updating and verification at multiple stages. To enable the probabilistic analysis of high dimensional systems, a highly efficient adaptive Markov Chain Monte Carlo solution procedure will be developed and embedded into the analysis framework. This framework allows us to evaluate simultaneously the structural uncertainties and their impact to vibration responses. If successful, the outcome of this research will lead to the concurrent advancements in both probabilistic structural analysis and Monte Carlo-based solution techniques. The information of structural uncertainties and their impact to vibration responses can be used to guide the robust design of structural products, and also be used to develop thresholds for vibration response-based online structural monitoring. The collaborative and interdisciplinary nature of the research will provide unique learning opportunities for students involved at all levels. The plan for dissemination will make results widely available, contributing to training analysts and engineers by presenting at professional conferences, publishing in refereed journals, and teaching at university-level courses.