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Health Monitoring of Civil Structures: A MCMC Approach Based on a Multi-Fidelity Deep Neural Network Surrogate^{ †}

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## Abstract

**:**

## 1. Introduction

## 2. SHM Methodology

#### 2.1. Datasets Definition

#### 2.2. Datasets Population

#### 2.3. MF-DNN Surrogate Model

#### 2.4. Damage Localization via MCMC

## 3. Virtual Experiment

`Tensorflow`-based

`Keras`API [22], running on an

`Nvidia GeForce RTX 3080`GPU card.

## 4. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 2.**Reconstruction capacity of ${\mathcal{NN}}_{\mathtt{MF}}$: (

**a**) regression over the POD-basis coefficients relative to a compressed LF signal; (

**b**) decompressed LF signal; (

**c**) regression over the HF signal.

**Figure 3.**Examples of MCMC analysis, in case of damage position (

**a**) close to the clamped side and (

**b**) far from the clamped side.

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**MDPI and ACS Style**

Torzoni, M.; Manzoni, A.; Mariani, S.
Health Monitoring of Civil Structures: A MCMC Approach Based on a Multi-Fidelity Deep Neural Network Surrogate. *Comput. Sci. Math. Forum* **2022**, *2*, 16.
https://doi.org/10.3390/IOCA2021-10889

**AMA Style**

Torzoni M, Manzoni A, Mariani S.
Health Monitoring of Civil Structures: A MCMC Approach Based on a Multi-Fidelity Deep Neural Network Surrogate. *Computer Sciences & Mathematics Forum*. 2022; 2(1):16.
https://doi.org/10.3390/IOCA2021-10889

**Chicago/Turabian Style**

Torzoni, Matteo, Andrea Manzoni, and Stefano Mariani.
2022. "Health Monitoring of Civil Structures: A MCMC Approach Based on a Multi-Fidelity Deep Neural Network Surrogate" *Computer Sciences & Mathematics Forum* 2, no. 1: 16.
https://doi.org/10.3390/IOCA2021-10889