Assessing structural reliability using real-time hybrid sub-structuring

Ligeikis, Connor; Christenson, Richard

doi:10.1504/ijlcpe.2020.10031040

Cited by 2 publications

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The proposed method, namely active-learning-kriging hybrid-simulation (AK-HS), relies on an adaptation of the active-learning kriging Monte Carlo simulation algorithm from the field of structural reliability [15]. The idea of combining the active-learning kriging Monte Carlo simulation algorithm and HS was originally proposed in [16] and applied in [17] to compute the probability of failure of a hybrid model with uncertain parameters. However, the computed failure probability has no utility outside the context of the examined prototype structure.…”

Section: Introductionmentioning

confidence: 99%

Training of a Classifier for Structural Component Failure based on Hybrid Simulation and Kriging

Abbiati¹,

Marelli²,

Ligeikis³

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

Hybrid simulation is a tool for discovering the inner workings of a tested substructure beyond the linear regime. Hybrid simulation is conducted to reproduce the response of a prototype in scaled or real time using a hybrid model that combines physical and numerical substructures interacting with each other in a feedback loop. As a result, the tested substructure interacts with a realistic assembly subjected to a credible loading scenario. The obtained low-quantity-high-value experimental data is used to conceive and calibrate computational models for nonlinear structural analysis in the current practice. Instead, this paper extends the scope of hybrid simulation to constructing a safe/failure state classifier for the tested substructure by adaptively designing a sequence of parametrized hybrid simulations. Such a classifier is intended to compute the state of any physical-substructure-like component within system-level numerical simulations. The proposed procedure is experimentally validated for a three-degrees-of-freedom hybrid model subjected to Euler buckling.

show abstract

Section: Introductionmentioning

confidence: 99%