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Presented By: Naval Architecture & Marine Engineering

NAME Prospectus Presentation for Kauhua Zhang

Kaihua Zhang

Kaihua Zhang will have his prospectus presentation “ Developing and Experimentally Validating of Dynamic Bayesian Network for System Reliability Prediction ”, Thursday, 11/15/2018 at 9:30 am in the Conference Room (232).

Committee Members:
Matthew Collette (Chair)
Allison John (Cognate)
Pingsha Dong
David Singer
Kevin Maki

Vessels and marine structures are subjected to degradation during their service, jeopardizing structural safety and shortening their service life. Numerical models of such structural systems are developed and relied on to simulate and ensure the systemintegrity. Such numerical models are the essential part of digital twins representing complex marine structures and providing enhanced forecasts of risk and lifecycle performance. Digital twins also require data fusion from observations or experiments to improve the numerical model agreements with the real-world structure. Due to the
infeasible of full-scale testing of marine structures, scale experiments are developed but few of them reflect many of the properties of large and complex marine structures.Thus, an experiment must be designed to mimic the multiple degradation process and retain structural redundancy so that a single element failure will not remove all load carrying capacity. Dynamic Bayesian network (DBN) can model the degradation process of structure but its performance has not been validated by experiments. Therefore, the proposed designs an experiment to mimic the properties of marine structure and develops a corresponding numerical model whose performance is validated by the designed experiment. To mimic the interdependence, redundancy and component-to-system level
performance of marine structures in degradation, a hexagon tension specimen with four propagating fatigue cracks, one on each corner, is designed and tested. The applied loading cycles and corresponding crack lengths are recorded as the major time-varying data of degradation state. Two methods of measuring crack length are developed based on computer vision and digital image correlation. To complement the test specimen, a DBN is constructed to predict the crack length and system reliability with observation input. The network models the time-varying process with sequential slices. The dependence among components are controlled by hyperparameters and are integrated into complex system behavior to reflect the structure from the component level to the
system level. The performance of the DBN is tested and validated by the data gathered from the experiment of hexagon specimen.

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