Doctor of Philosophy, The Ohio State University, 2024, Industrial and Systems Engineering

This dissertation addresses two applied problems relating to images. The first relates to images of pipeline corrosion and the second relates to images of the human brain and individuals with Attention-Deficit/Hyperactivity Disorder (ADHD). The corrosion of oil and gas pipelines is important because there are thousands of leaks every year costing billions of dollars for cleanups. ADHD is important because a substantial fraction of the world population has the disorder causing significant suffering and hundreds of billions of dollars of losses to the world economy. To address both image analysis problems, novel statistical and operations research techniques are proposed which have potentially wide applicability. Relating to pipeline corrosion, an established simulation method is called the “voxel” method which permits predictions about how images and pipelines or other media will change as corrosion evolves. In most realistic cases, we find that the parameter values or “inputs” (Xs) needed to run the simulation are unknown. We only have the images which are essentially outputs (Ys) which can be generated by real world experiments or simulations. The phenomenon of having incomplete inputs for simulation is common in many engineering and science situations and a critical challenge for both people and artificial intelligence. We and others have called this important subject, “empirical forward-inverse estimation” since we can gather data (empirically) in the forward manner progressing from assumed inputs (Xs) to measured outputs (Ys) and then generate inverse predictions from Ys to Xs. With (hopefully) accurately estimated X values, the experimental setup or simulation can then predict the future corrosion evolution and whether repair in critically needed. Relating to forward-inverse analyses, 24 variants of an established two stage method or framework are studied in relation to enhanced inverse prediction accuracy for two test cases including pipeline corrosion (open full item for complete abstract)

Committee: Theodore T. Allen (Advisor); William (Bill) Notz (Committee Member); Samantha Krening (Committee Member); Marat Khafizov (Committee Member) Subjects: Engineering; Industrial Engineering; Materials Science; Statistics

Master of Science, University of Akron, 2015, Civil Engineering

Understanding and predicting the behavior of structures under specific operating conditions is a fundamental task of structural engineers. Scientific principles are used to model the characteristics of a material's response to these various mechanical loads. Using experimental data, constitutive models can be created that provide a mathematical description of a materials response. However, these constitutive models require numerous parameters to be identified. In order to calculate these parameters, inverse parameter identification algorithms can be used. These constitutive models apply a homogenous distribution of the material parameters across a structural component. However, in reality there is often a heterogeneous distribution of these material parameters across the structure. This can be due to a variety of reasons including the characteristics of the raw material, geometry, manufacturing processes, fatigue and damage. In order to model this heterogeneous distribution, stochastic methods can be deployed. In this research, an inverse parameter identification method known as the Self-Optimizing Inverse Methodology (Self-OPTIM) will be used to create a powerful and easy to use software framework for parameter identification. This software framework includes capabilities to parallelize finite element simulation to reduce the time of optimization. In addition, this framework will include a stochastic methodology that can be used to model heterogeneous distributions of material parameters across a structural component. Using this software, the capabilities of Self-OPTIM will be tested on various constitutive models to demonstrate its ease of use as well as its superiority to other methods using boundary information as its primary input.

Committee: Gunjin Yun Dr. (Advisor); Robert Goldberg Dr. (Committee Member); Weislaw Binienda Dr. (Committee Member) Subjects: Civil Engineering

Doctor of Philosophy, University of Akron, 2012, Civil Engineering

In the field of computational mechanics, there has been a very challenging problem, which is the characterization of heterogeneous media with randomly distributed material properties. In reality, no material is homogeneous and deterministic in nature and it has been well-known that randomness in microstructures and properties of materials could significantly influence scatter of structural response at larger scales. Therefore, stochastic characterization of heterogeneous materials has increasingly received attention in various engineering and science fields. In order to deal with this challenging problem, two major challenges need to be addressed: 1) developing an efficient modeling technique to discretize the material uncertainty in the stochastic domain and 2) developing a robust and general inverse identification computational framework that can estimate parameters related to material uncertainties. In this dissertation, two major challenges have been addressed by proposing a robust inverse analysis framework that can estimate parameters of material constitutive models based on a set of limited global boundary measurements and combining the framework with a general stochastic finite element analysis tool. Finally a new stochastic inverse analysis framework has been proposed, which has a novel capability of modeling effects of spatial variability of both linear and nonlinear material properties on macroscopic material and structural response. By inversely identifying statistical parameters (e.g. spatial mean, spatial variance, spatial correlation length, and random variables) related to spatial randomness of material properties, it allows for generating statistically equivalent realizations of random distributions of linear and nonlinear material properties and their applications to the development of probabilistic structural models. First, a robust inverse identification framework, called the Self-Optimizing Inverse Method (Self-OPTIM), has been developed. Unli (open full item for complete abstract)

Committee: Gun Jin Yun Dr. (Advisor); Wieslaw Binienda Dr. (Committee Member); Ernian Pan Dr. (Committee Member); Xiaosheng Gao Dr. (Committee Member); Kevin Kreider Dr. (Committee Member) Subjects: Civil Engineering; Engineering; Experiments; Materials Science; Mathematics