- Explainability of convolutional neural networks for damage diagnosis using transmissibility functions. In: Structures, v. 69 (November 2024). (2024):
- Particle filter-based fatigue damage prognosis by fusing multiple degradation models. In: Structural Health Monitoring, v. 23, n. 5 (Februar 2024). (2024):
- Deep learning-based analysis to identify fluid-structure interaction effects during the response of blast-loaded plates. In: International Journal of Protective Structures, v. 15, n. 4 (Februar 2024). (2024):
- Enhancing Lamb wave-based damage diagnosis in composite materials using a pseudo-damage boosted convolutional neural network approach. In: Structural Health Monitoring, v. 23, n. 3 (September 2023). (2023):
- Vibration‐based structural health monitoring exploiting a combination of convolutional neural networks and autoencoders for temperature effects neutralization. In: Structural Control and Health Monitoring, v. 29, n. 11 (September 2022). (2022):
- Particle filter-based delamination shape prediction in composites subjected to fatigue loading. In: Structural Health Monitoring, v. 22, n. 3 (September 2022). (2022):
- Particle filter‐based hybrid damage prognosis considering measurement bias. In: Structural Control and Health Monitoring, v. 29, n. 4 (14 März 2022). (2022):
- On the mitigation of the RAPID algorithm uneven sensing network issue employing averaging and Gaussian blur filtering techniques. In: Composite Structures, v. 278 (Dezember 2021). (2021):
- Fatigue damage diagnosis and prognosis of an aeronautical structure based on surrogate modelling and particle filter. In: Structural Health Monitoring, v. 20, n. 5 (Dezember 2020). (2020):
- Advanced Monte Carlo Methods and Applications. In: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, v. 3, n. 4 (Dezember 2017). (2017):
- Global reliability sensitivity analysis by Sobol-based dynamic adaptive kriging importance sampling. In: Structural Safety, v. 87 (November 2020). (2020):
- Particle filtering‐based adaptive training of neural networks for real‐time structural damage diagnosis and prognosis. In: Structural Control and Health Monitoring, v. 26, n. 12 (12 November 2019). (2019):
- A particle filter-based model selection algorithm for fatigue damage identification on aeronautical structures. In: Structural Control and Health Monitoring, v. 24, n. 11 (November 2017). (2017):