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Dataset for thesis "Development and Experimental Validation of Dynamic Bayesian Networks for System Reliability Prediction"

Creator:

Zhang, Kaihua and Collette, Matthew D.

Description:

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.

Keyword:

System Reliability, Dynamic Bayesian Networks, Fatigue Experiment, Crack Length Measurement, Experimental Validation, Reliability Prediction

Citation to related publication:

"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

Discipline:

Engineering

Dataset for thesis "Development and Experimental Validation of Dynamic Bayesian Networks for System Reliability Prediction"

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Dataset for thesis "Development and Experimental Validation of Dynamic Bayesian Networks for System Reliability Prediction"

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