Date of Award
5-2023
Document Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Civil Engineering
Committee Chair/Advisor
Dr. M.Z. Naser
Committee Member
Dr. Laura Redmond
Committee Member
Dr. Brandon Ross
Committee Member
Dr. Qiushi Chen
Abstract
Inverse problems involve extracting the internal structure of a physical system from noisy measurement data. In many fields, the Bayesian inference is used to address the ill-conditioned nature of the inverse problem by incorporating prior information through an initial distribution. In the nonparametric Bayesian framework, surrogate models such as Gaussian Processes or Deep Neural Networks are used as flexible and effective probabilistic modeling tools to overcome the high-dimensional curse and reduce computational costs. In practical systems and computer models, uncertainties can be addressed through parameter calibration, sensitivity analysis, and uncertainty quantification, leading to improved reliability and robustness of decision and control strategies based on simulation or prediction results. However, in the surrogate model, preventing overfitting and incorporating reasonable prior knowledge of embedded physics and models is a challenge. Suspended Nonstructural Systems (SNS) pose a significant challenge in the inverse problem. Research on their seismic performance and mechanical models, particularly in the inverse problem and uncertainty quantification, is still lacking. To address this, the author conducts full-scale shaking table dynamic experiments and monotonic & cyclic tests, and simulations of different types of SNS to investigate mechanical behaviors. To quantify the uncertainty of the inverse problem, the author proposes a new framework that adopts machine learning-based data and model driven stochastic Gaussian process model calibration to quantify the uncertainty via a new black box variational inference that accounts for geometric complexity measure, Minimum Description length (MDL), through Bayesian inference. It is validated in the SNS and yields optimal generalizability and computational scalability.
Recommended Citation
Qin, Zhiyuan, "Machine Learning-Based Data and Model Driven Bayesian Uncertanity Quantification of Inverse Problems for Suspended Non-structural System" (2023). All Dissertations. 3317.
https://tigerprints.clemson.edu/all_dissertations/3317
Included in
Artificial Intelligence and Robotics Commons, Civil Engineering Commons, Computational Engineering Commons, Data Science Commons, Design of Experiments and Sample Surveys Commons, Dynamical Systems Commons, Engineering Mechanics Commons, Engineering Physics Commons, Longitudinal Data Analysis and Time Series Commons, Mechanical Engineering Commons, Non-linear Dynamics Commons, Numerical Analysis and Scientific Computing Commons, Risk Analysis Commons, Structural Engineering Commons, Structural Materials Commons, Theory and Algorithms Commons