Doctor of Philosophy, The Ohio State University, 2019, Civil Engineering

Uncertainties are ubiquitous in various engineering and science fields. Examples include analysis and design of structures and infrastructure systems against natural or manmade hazards, rocket and satellite design in aerospace engineering and safety analysis in nuclear engineering. To enhance the performance of those systems, actions taken by designers and decision-makers should be toward a set of performance objectives with higher reliability or resilience. Moreover, as sensing technologies are maturing and becoming more cost efficient, allowing their implementation at large scales, information about the state of the built and natural environments is becoming more available. This information can be leveraged to reevaluate or update forecasts of the performance of these systems and enhance confidence in our forecasts of the future performance. Analysis of the new information can therefore lead to more effective risk-informed decisions. Uncertainty Quantification (UQ) techniques such as reliability analysis and updating can help with quantitative assessment and real-time updates of infrastructure performance through the estimation of probability of failing to meet one or a set of objectives. The state-of-the-art techniques based on surrogate models, such as Kriging, open new avenues for reliability analysis by adaptively learning the shape of the limit state and substituting the originally time-consuming performance function with the estimated one. However, (I) the process of unnecessary training, (II) lack of accuracy measurement for the failure probability estimate, (III) high computational demand for high-dimensional problems, and (IV) lack of capability to perform reliability analysis with real-time updating still remain as significant challenges. To address the aforementioned limitations, this study offers the following novel contributions: - A methodology called Reliability analysis through Error rate-based Adaptive Kriging (REAK) is proposed to significantl (open full item for complete abstract)

Committee: Abdollah Shafieezadeh (Advisor); Halil Sezen (Committee Member); Alper Yilmaz (Committee Member); Jieun Hur (Committee Member) Subjects: Civil Engineering; Engineering; Mechanical Engineering; Operations Research

Doctor of Philosophy, The Ohio State University, 2022, Civil Engineering

Uncertainty quantification is important in many engineering and scientific domains, as uncertainties, of both aleatory and epistemic types, are ubiquitous and inevitable since the complete knowledge cannot be achieved. The probability of failure quantifies the probability of a system failing to meet a specific performance requirement. It is a vital measurement of performance when uncertainties are considered, and it can facilitate the design optimization and decision making for critical infrastructure systems. The computational costs of uncertainty quantification are often prohibitive due to the nature of multi-query analysis and expensive numerical models. Surrogate models can be used to facilitate the reliability analysis. Kriging is the among the most popular surrogate models for reliability analysis due to its capability of providing uncertainty information. How to best utilize the simulation data to construct the Kriging model is a primary research topic in the reliability domain. This dissertation offers the following novel contributions to this research topic: • A novel methodology for adaptive Kriging reliability methods is proposed. It considers the global impact of adding new training points and focuses on reducing the error in the most effective manner. • An effective multi-fidelity reliability method is proposed. The information source and training points can be selected simultaneously to achieve optimal construction of the surrogate model. • A two-phase approach for reliability updating with adaptive Kriging is proposed. The error of posterior failure probability introduced by the Kriging model is quantified. • Adaptive Kriging method is integrated with value of information analysis, and a knowledge sharing scheme is developed to enhance the training efficiency. While surrogate models such as Kriging substantially reduce the computational cost of multi-query analyses, they still require costly simulations of complex computational models. (open full item for complete abstract)

Committee: Abdollah Shafieezadeh (Advisor); Halil Sezen (Committee Member); Jieun Hur (Committee Member) Subjects: Civil Engineering