December 4, 2025Engineering With ComputersOpen Access

Enhancing Bayesian model updating in structural health monitoring via learnable mappings

View Full Paper

Ask AI

Bookmark

Enhanced parameter estimation achieved with deep neural networks, improving structural health monitoring.
Markov chain Monte Carlo sampling effectively employs a learnable feature extractor for optimal results.
Assessment includes synthetic case studies demonstrating the utility of a multi-fidelity surrogate model.
Supports the integration of artificial neural networks, promoting efficiency in evaluating structural health parameters.

Discussion

No takes yet. Share an insight, caveat, or question.

Implication

This research reveals improved parameter estimation in structural health monitoring using deep neural networks, indicating advancements in model efficiency.

Cite This Study

Matteo Torzoni (2025) studied this question.

synapsesocial.com/papers/6930e8bdea1aef094cca3301 https://doi.org/10.1007/s00366-025-02224-x

View Full Paper

Ask AI

Bookmark

Implication

This research reveals improved parameter estimation in structural health monitoring using deep neural networks, indicating advancements in model efficiency.

Enhancing Bayesian model updating in structural health monitoring via learnable mappings

Key Points

Discussion

Implication

Cite This Study

Enhancing Bayesian model updating in structural health monitoring via learnable mappings

Key Points

Discussion

Implication

Cite This Study