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Ana Fernandez Navamuel

Postdoc Fellow

M +34 946 567 842
F +34 946 567 842
E afernandez@bcamath.org

BCAM-TECNALIA PostDoc Fellow

Deep Neural Network for damage detection in Infante Dom Henrique bridge using multi-sensor data

Fernandez-Navamuel, A.; Pardo, D.; Magalhães, F.; Zamora-Sánchez, D.; Omella, A. J.; Garcia-Sanchez, D. (2024-03-22)

This paper proposes a data-driven approach to detect damage using monitoring data from the Infante Dom Henrique bridge in Porto. The main contribution of this work lies in exploiting the combination of raw measurements ...

Bridge damage identification under varying environmental and operational conditions combining Deep Learning and numerical simulations

Fernandez-Navamuel, A.; Pardo, D.; Garcia-Sanchez, D.; Zamora-Sánchez, D.; Magalhães, F.; Omella, Ángel J. (2023-10)

This work proposes a novel supervised learning approach to identify damage in operating bridge structures. We propose a method to introduce the effect of environmental and operational conditions into the synthetic damage ...

Damage identification in bridges combining deep learning and computational mechanic

Fernandez-Navamuel, A. (2022-12-12)

Civil infrastructures, such as bridges, are critical assets for society and the economy. Many of them have already reached their expected life and withstand loadings that exceed the design specifications. Besides, bridges ...

Combined model-based and machine learning approach for damage identification in bridge type structures

Fernandez-Navamuel, A.; Zamora-Sánchez, D.; Armijo-Prieto, A.; Varona-Poncela, T.; García-Sánchez, D.; García-Villena, F.; Ruiz-Cuenca, F. (2022-06)

In this work, we propose a combined approach of model-based and machine learning techniques for damage identification in bridge structures. First, a finite element model is calibrated with real data from experimental ...

Supervised Deep Learning with Finite Element simulations for damage identification in bridges

Fernandez-Navamuel, A.; Zamora-Sánchez, Diego; Omella, Ángel J.; Pardo, D.; Garcia-Sanchez, David; Magalhães, Filipe (2022-04-15)

This work proposes a supervised Deep Learning approach for damage identification in bridge structures. We employ a hybrid methodology that incorporates Finite Element simulations to enrich the training phase of a Deep ...

Deep learning enhanced principal component analysis for structural health monitoring

Fernandez-Navamuel, A.; Magalhães, F.; Zamora-Sánchez, D.; Omella, A. J.; Garcia-Sanchez, D.; Pardo, D. (2022-01-18)

This paper proposes a Deep Learning enhanced Principal Component Analysis (PCA) approach for outlier detection to assess the structural condition of bridges. We employ a partially explainable autoencoder architecture to ...

Deep learning enhanced principal component analysis for structural health monitoring

Fernandez-Navamuel, A.; Magalhães, Filipe; Zamora-Sánchez, Diego; Omella, Ángel J.; Garcia-Sanchez, David; Pardo, D. (2022-01-01)

This paper proposes a Deep Learning Enhanced Principal Component Analysis (PCA) approach for outlier detection to assess the structural condition of bridges. We employ partially explainable autoencoder architecture to ...

Vibration-based SHM strategy for a real time alert system with damage location and quantification

Fernandez-Navamuel, A.; Zamora-Sánchez, D.; Varona-Poncela, T.; Jiménez-Fernández, C.; Díez-Hernández, J.; García-Sánchez, D. (2021-01)

We present a simple and fully automatable vibration-based Structural Health Monitoring (SHM) alert system. The proposed method consists in applying an Automated Frequency Domain Decomposition (AFDD) algorithm to obtain the ...

Bearing assessment tool for longitudinal bridge performance

Garcia-Sanchez, D.; Fernandez-Navamuel, A.; Zamora, D.; Alvear, D.; Pardo, D. (2020-09)

This work provides an unsupervised learning approach based on a single-valued performance indicator to monitor the global behavior of critical components in a viaduct, such as bearings. We propose an outlier detection ...