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+34 946 567 842
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+34 946 567 842
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afernandez@bcamath.org
Information of interest
- Orcid: -0002-0655-6072
BCAM-TECNALIA PostDoc Fellow
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Bridge damage identification under varying environmental and operational conditions combining Deep Learning and numerical simulations
(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 ...
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Combined model-based and machine learning approach for damage identification in bridge type structures
(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 ...
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Supervised Deep Learning with Finite Element simulations for damage identification in bridges
(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 ...
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Deep learning enhanced principal component analysis for structural health monitoring
(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 ...
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Vibration-based SHM strategy for a real time alert system with damage location and quantification
(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 ...
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Bearing assessment tool for longitudinal bridge performance
(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 ...