The 7th Training Week of ENHAnCE was celebrated in Milano, Italy

Uncertainty quantification in Neural Networks by Approximate Bayesian Computation: Application to fatigue in composite materials.

Particle filter-based hybrid damage prognosis considering measurement bias.

Reduction of Petri net maintenance modeling complexity via Approximate Bayesian Computation.

Risk-based maintenance strategy selection for wind turbine composite blades.

Numerical simulation-aided particle filter-based damage prognosis using Lamb waves.

Structural digital twin framework: Formulation and technology integration.

Guided waves-based damage identification in plates through an inverse Bayesian process. 

Particle filter-based delamination shape prediction in composites subjected to fatigue loading

A cross-sectorial review of the current and potential maintenance strategies for composite structures

Asset management modelling approach integrating structural health monitoring data for composite components of wind turbine blades

Self-adaptative optimized maintenance of offshore wind turbines by intelligent Petri nets

An assessment of different reinforcement learning methods for creating a decision support system based on the Petri Net model

Probabilistic safety Assesment in Composite Materials using BNN by ABC-SS

Interpretable neural network with limited weights for constructing simple and explainable HI using SHM

Intelligent health indicator construction for prognostics of composite structures utilizing a semi-supervised deep neural network and SHM data.

Intelligent health inidicator based on semi-supervised learning utilizing acoustic emission data.

A wind turbine blade leading edge rain erosion computational framework.

Physics-guided Bayesian neural networks by ABC-SS: Application to reinforced concrete.

 

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Code

code D5.2  : Code related to the Integration of communications and SHM data (demonstrator, Deliverable 5.2)