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