Structural Health Monitoring & Machine Learning, Volume 12: Proceedings of the 43rd IMAC, A Conference and Exposition on Structural Dynamics, 2025, the twelfth volume of twelve from the Conference brings together contributions to this important area of research and engineering. The collection presents early findings and case studies on fundamental and applied aspects of the Structural Health Monitoring, including papers on:
- Bayesian Methods for Model Inference
- Health Monitoring using dynamic measurements
- Health Monitoring using Digital Twinning
- SHM using Machine Learning
- Case studies of SHM on real-world dynamic systems
- Other Innovative SHM Methods
Structural health Monitoring &. Machine Learning, Volume 12: Proceedings of the 43rd IMAC, A Conference and Exposition on Structural Dynamics, 2025, the twelth volume of twelve from the Conference brings together contributions to this important area of research and engineering.
1. Theoretical Foundations and Practical Applications of Damage
Detection Using Autocovariance Functions
2. On the Real Time Tightness
Measurement of Complex Shaped Flanges
3. Parameter Rejection in
Sensitivity-based Model Updating using Output Feedback Eigenstructure
Assignment
4. Structural Health Monitoring of a Ferry Quay: Instrumentation
and Impact of Tidal Levels on Modal Parameters
5. Outcomes from Field
Measurements on the Magerholm Ferry Quay: System Identification, Finite
Element Model Updating and Sensitivity Analysis
6. A Robust Data-Driven
Algorithm for Early Damage Detection in Structural Health Monitoring
7.
Real-Time Structural Health Assessment of a Tension Rod Assembly Using
Machine Learning
8. Multi-Bridge Indirect Structural Health Monitoring:
Leveraging Big Data and Drive-By Crowdsensing Techniques
9. A Comparative
Study of Feature Selection Methods for Wind Turbine Gearbox Bearing Fault
Prognosis
10. Damage Identification on Gear Drivetrains Using Neural Networks
Trained by High-Fidelity Multibody Simulation Data
11. Advanced Condition
Monitoring framework for CFRP Gear Drivetrains Using Machine Learning and
Multibody Dynamics Simulations
12. On the use of Statistical Learning Theory
for model selection in Structural Health Monitoring
13. Full-field
Measurements for Anomaly Detection of Mechanical Systems using Convolutional
Neural Networks and LSTM Networks
14. A Generative Modeling Approach for the
Translation of Operational Variables to Short-term Vibrations
15. Effective
Structural Health Monitoring of Rotating Propellers using Asynchronous
Neuromorphic Tracking
16. Estimating Damage Detection of an Aircraft
Component with Machine Learning Models
17. Physics-Informed Machine Learning
for Advanced Structural Damage Detection and Localization
18. Damage
Detection Strategy Based on PCA/Mode-Shapes Developed on a Laboratory Truss
Girder Subjected to Environmental Variations
Brian Damiano, Babak Moaveni, Antonio De Luca, Keith Worden
Rohkem infot Taylor & Francis e-raamatute kohta