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The Self-Optimizing Inverse Methodology for Material Parameter Identification and Distributed Damage Detection

Weaver, Josh
http://rave.ohiolink.edu/etdc/view?acc_num=akron1428316985

Abstract Details

2015, Master of Science, University of Akron, 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.
Gunjin Yun, Dr. (Advisor)
Robert Goldberg, Dr. (Committee Member)
Weislaw Binienda, Dr. (Committee Member)
123 p.
Civil Engineering
inverse parameter estimation; parallelization; Self-optim; ABAQUS; sensitivity analysis; stochastic; damage detection

Recommended Citations

Citations

  • Weaver, J. (2015). The Self-Optimizing Inverse Methodology for Material Parameter Identification and Distributed Damage Detection [Master's thesis, University of Akron]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=akron1428316985

    APA Style (7th edition)

  • Weaver, Josh. The Self-Optimizing Inverse Methodology for Material Parameter Identification and Distributed Damage Detection. 2015. University of Akron, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=akron1428316985.

    MLA Style (8th edition)

  • Weaver, Josh. "The Self-Optimizing Inverse Methodology for Material Parameter Identification and Distributed Damage Detection." Master's thesis, University of Akron, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=akron1428316985

    Chicago Manual of Style (17th edition)

akron1428316985
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© 2015, some rights reserved.Creative Commons License The Self-Optimizing Inverse Methodology for Material Parameter Identification and Distributed Damage Detection by Josh Weaver is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License. Based on a work at etd.ohiolink.edu.
This open access ETD is published by University of Akron and OhioLINK.