Eloi Figueiredo 
- : Co-Kriging strategy for structural health monitoring of bridges. In: Structural Health Monitoring.
- (2024): A Comprehensive Study on Unsupervised Transfer Learning for Structural Health Monitoring of Bridges Using Joint Distribution Adaptation. In: Infrastructures, v. 9, n. 8 (3 August 2024).
- (2024): Hybrid Approach for Supervised Machine Learning Algorithms to Identify Damage in Bridges. In: Journal of Bridge Engineering (ASCE), v. 29, n. 8 (August 2024).
- (2024): Unsupervised transfer learning for structural health monitoring of urban pedestrian bridges. In: Journal of Civil Structural Health Monitoring, v. 14, n. 6 (April 2024).
- : Bayesian data-driven framework for structural health monitoring of composite structures under limited experimental data. In: Structural Health Monitoring.
- : Impact of climate change on long-term damage detection for structural health monitoring of bridges. In: Structural Health Monitoring.
- (2023): Transfer Learning for Structural Health Monitoring in Bridges That Underwent Retrofitting. In: Buildings, v. 13, n. 9 (23 August 2023).
- (2023): A Roadmap for an Integrated Assessment Approach to the Adaptation of Concrete Bridges to Climate Change. In: Journal of Bridge Engineering (ASCE), v. 28, n. 6 (Juni 2023).
- (2023): Transfer Learning to Enhance the Damage Detection Performance in Bridges When Using Numerical Models. In: Journal of Bridge Engineering (ASCE), v. 28, n. 1 (Januar 2023).
- (2022): Damage Detection Approach for Bridges under Temperature Effects using Gaussian Process Regression Trained with Hybrid Data. In: Journal of Bridge Engineering (ASCE), v. 27, n. 11 (November 2022).
- (2022): Damage quantification using transfer component analysis combined with Gaussian process regression. In: Structural Health Monitoring, v. 22, n. 2 (Mai 2022).
- (2022): Three decades of statistical pattern recognition paradigm for SHM of bridges. In: Structural Health Monitoring, v. 21, n. 6 (März 2022).
- (2022): Reliability of probabilistic numerical data for training machine learning algorithms to detect damage in bridges. In: Structural Control and Health Monitoring, v. 29, n. 7 (März 2022).
- (2022): Investigation on the dynamic impact factor of a concrete filled steel tube butterfly arch bridge. In: Engineering Structures, v. 252 (Februar 2022).
- (2021): Damage‐sensitive feature extraction with stacked autoencoders for unsupervised damage detection. In: Structural Control and Health Monitoring, v. 28, n. 5 (12 April 2021).
- (2010): Machine learning algorithms for damage detection under operational and environmental variability. In: Structural Health Monitoring, v. 10, n. 6 (November 2010).
- (2016): A global expectation-maximization based on memetic swarm optimization for structural damage detection. In: Structural Health Monitoring, v. 15, n. 5 (August 2016).
- (2018): Deep principal component analysis: An enhanced approach for structural damage identification. In: Structural Health Monitoring, v. 18, n. 5-6 (September 2018).
- (2011): Influence of the Autoregressive Model Order on Damage Detection. In: Computer-Aided Civil and Infrastructure Engineering, v. 26, n. 3 ( 2011).
- (2019): Finite Element–Based Machine-Learning Approach to Detect Damage in Bridges under Operational and Environmental Variations. In: Journal of Bridge Engineering (ASCE), v. 24, n. 7 (Juli 2019).
- (2017): Genetic-based EM algorithm to improve the robustness of Gaussian mixture models for damage detection in bridges. In: Structural Control and Health Monitoring, v. 24, n. 3 (März 2017).
- (2010): A structural decomposition approach for detecting, locating, and quantifying nonlinearities in chain-like systems. In: Structural Control and Health Monitoring, v. 17, n. 7 (November 2010).
- (2014): A Bayesian approach based on a Markov-chain Monte Carlo method for damage detection under unknown sources of variability. In: Engineering Structures, v. 80 (Dezember 2014).
- (2013): Linear approaches to modeling nonlinearities in long-term monitoring of bridges. In: Journal of Civil Structural Health Monitoring, v. 3, n. 3 (April 2013).
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