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Title page for ETD etd-08222014-163854

Type of Document Dissertation
Author Mullins, Joshua Grady
URN etd-08222014-163854
Title Resource Allocation for Uncertainty Quantification and Reduction
Degree PhD
Department Civil Engineering
Advisory Committee
Advisor Name Title
Sankaran Mahadevan Committee Chair
Alejandro Strachan Committee Member
Angel Urbina Committee Member
Caglar Oskay Committee Member
Prodyot Basu Committee Member
Ravindra Duddu Committee Member
  • resource allocation
  • model calibration
  • model validation
  • model selection
  • test selection
  • uncertainty quantification
Date of Defense 2014-08-19
Availability unrestricted
This dissertation explores resource allocation methods for quantification and reduction of prediction uncertainty from computational models, in order to enable risk assessment and decision-making in engineering systems. The proposed methods focus primarily upon epistemic uncertainty due to approximate models and inadequate data, since it can be reduced by refining the models or collecting additional information. In order to reduce the uncertainty contribution from each of these sources, there is a tradeoff decision of cost vs. value. Methods for selecting among available modeling options of different fidelity are explored with the goal of simultaneously minimizing prediction error and computational expense. Then, validation methods for incorporating data uncertainty are considered, and a methodology for directly linking the validation result to the prediction of interest is proposed. A test selection approach is developed that explores the impact of data for both model calibration and model validation on the prediction of interest. Finally, the risk-based resource allocation problem is formulated. The proposed formulation motivates the total spending in the overall uncertainty quantification framework and provides insight about how much is economically efficient.
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