Structural Health Monitoring & Machine Learning, Vol. 12

Proceedings of the 43rd IMAC, A Conference and Exposition on Structural Dynamics 2025
Herausgeber: De Luca, Antonio; Worden, Keith; Damiano, Brian; Moaveni, Babak

• Gebundenes Buch

Produktbeschreibung

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

Produktdetails

• Verlag: River Publishers
• Seitenzahl: 148
• Erscheinungstermin: 22. Januar 2026
• Englisch
• Abmessung: 280mm x 210mm
• ISBN-13: 9788743801573
• ISBN-10: 8743801579
• Artikelnr.: 75722220

Autorenporträt

Brian Damiano, Babak Moaveni, Antonio De Luca, Keith Worden

Inhaltsangabe

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