This Ph.D. research focuses on two subject areas: experimental and numerical
model, which serves as two essential parts of a digital twin. A digital twin contains
models of real-world structures and fuses data from observations of the structures
and scale experiment to pull the models into better agreement with the real world.
Digital twin models have the promise of representing complex marine structures and
providing enhanced lifecycle performance and risk forecasts. Experimentally verifying
the updating approaches is necessary but rarely performed. Thus, the proposed
work is designing an experiment and developing a numerical model updated by the experimental data.
The dataset contains all the data collected in the experiment of a four-crack hexagon-
shaped specimen is presented, designed to mimic many of the properties of complex
degrading marine structural systems, such as crack interaction, component inter-
dependence, redundant load path, and non-binary failure.
"Evaluating Crack Growth Prediction in Structural Systems with Dynamic Bayesian Networks", submitted to Computers and Structure and Zhang, K., & Collette, M. (2021). Experimental investigation of structural system capacity with multiple fatigue cracks. Marine Structures, 78, 102943. https://doi.org/10.1016/j.marstruc.2021.102943
