College of Computing and Digital Media Dissertations

Date of Award

Fall 11-22-2021

Degree Type


Degree Name

Master of Science (MS)


School of Computing

First Advisor

Ilyas Ustun, PhD

Second Advisor

Jacob Furst, PhD

Third Advisor

Rafaella Settimi-Woods, PhD


Computational progressive failure analysis (PFA) is vital for the design, verification, and validation of carbon fiber reinforced polymer (CFRP) composites. However, the computational cost of PFA is usually high due to the complexity of the model. The damage initiation criterion is one of the essential components of a PFA code to determine the transition of a material’s state from pristine or microscopically damaged to macroscopically damaged. In this thesis, data-driven models are developed to determine the matrix damage initiation based on the Mohr-Coulomb model and Hashin model. For 2D plane stress states, the computational cost for determining damage initiation can be dramatically reduced by implementing a Binary Search (BS) algorithm and predictive machine learning models. We have demonstrated the usage of BS and the training and evaluation of machine learning models as alternative methods to determine macroscale matrix damage initiation. With the data-driven methods, over 99% of the computational time has been saved, while the predictive accuracy stays 99.9% from the traditional approach. For a 3D stress state, 87% of the computational time is saved with the predictive accuracy of 94.7%.



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