Structural Health Monitoring & Machine Learning, Vol. 12

Structural Health Monitoring & Machine Learning, Vol. 12
Proceedings of the 43rd IMAC, A Conference and Exposition on Structural Dynamics 2025

Conference Proceedings of the Society for Experimental Mechanics Series

Structural Health Monitoring & Machine Learning, Vol. 12
Proceedings of the 43rd IMAC, A Conference and Exposition on Structural Dynamics 2025

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

ISBN: 9788743801573 e-ISBN: 9788743801696

Available: August 2025

doi: https://doi.org/10.13052/97887-438-0157-3

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. 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
autocovariance function, spatiotemporal, redundant, Flange tightness, state monitoring, virtual sensor, SCADA data, Wind Turbine, Prognosis, Structural health monitoring; Domain adaptation

Article 1: Theoretical Foundations and Practical Applications of Damage Detection Using Autocovariance Functions
by Jyrki Kullaa
https://doi.org/10.13052/97887-438-0157-3_1


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Article 2: On the Real Time Tightness Measurement of Complex Shaped Flanges
by Wolfgang Witteveen, Simon Desch, Lukas Koller
https://doi.org/10.13052/97887-438-0157-3_2


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Article 3: Parameter Rejection in Sensitivity-based Model Updating using Output Feedback Eigenstructure Assignment
by Martin D. Ulriksen, Dionisio Bernal
https://doi.org/10.13052/97887-438-0157-3_3


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Article 4: Structural Health Monitoring of a Ferry Quay: Instrumenta- tion and Impact of Tidal Levels on Modal Parameters
by Luigi Sibille, Torodd Skjerve Nord, Ba Tung Le, Bartosz Siedziako, Alice Cicirello
https://doi.org/10.13052/97887-438-0157-3_4


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Article 5: Outcomes from Field Measurements on the Magerholm Ferry Quay: System Identification, Finite Element Model Updating and Sensitivity Analysis
by Ba Tung Le, Bartosz Siedziako, Torodd Skjerve Nord, Luigi Sibille
https://doi.org/10.13052/97887-438-0157-3_5


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Article 6: A Robust Data-Driven Algorithm for Early Damage Detection in Structural Health Monitoring
by Luigi Severa, Silvia Milana, Nicola Roveri, Eleonora Maria Tronci, Antonio Culla, Antonio Carcaterra
https://doi.org/10.13052/97887-438-0157-3_6


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Article 7: Real-Time Structural Health Assessment of a Tension Rod Assembly Using Machine Learning
by Ahmad Rababah, Osama Abdeljaber, Aniston Cumbie, Lauren Harpenau, and Onur Avci
https://doi.org/10.13052/97887-438-0157-3_7


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Article 8: Multi-Bridge Indirect Structural Health Monitoring: Leveraging Big Data and Drive-By Crowdsensing Techniques
by Jiangyu Zeng, Qipei Mei, Mustafa Gul
https://doi.org/10.13052/97887-438-0157-3_8


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Article 9: A Comparative Study of Feature Selection Methods for Wind Turbine Gearbox Bearing Fault Prognosis
by Feras Abla, Mohammad Hesam Soleimani-Babakamali, Sahabeddin Rifai, Ahmad Rababah, Shawn Sheng, Ertugrul Taciroglu, Serkan Kiranyaz, Onur Avci
https://doi.org/10.13052/97887-438-0157-3_9


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Article 10: Damage Identification on Gear Drivetrains Using Neural Networks Trained by High-Fidelity Multibody Simulation Data
by J. Koutsoupakis, D. Giagopoulos, G. Karyofyllas, P. Seventekidis, S. Natsiavas
https://doi.org/10.13052/97887-438-0157-3_10


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Article 11: Advanced Condition Monitoring framework for CFRP Gear Drivetrains Using Machine Learning and Multibody Dynamics Simulations
by G. Karyofyllas, J. Koutsoupakis, P. Giagopoulos
https://doi.org/10.13052/97887-438-0157-3_11


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Article 12: On the use of Statistical Learning Theory for model selection in Structural Health Monitoring
by C. A. Lindley, N. Dervilis, K. Worden
https://doi.org/10.13052/97887-438-0157-3_12


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Article 13: Full-field Measurements for Anomaly Detection of Mechanical Systems using Convolutional Neural Networks and LSTM Networks
by Carlos Quiterio Gomez Munoz, Mariano Alberto Garcıa Vellisca, Celso T. do Cabo, Yujie Xi, Zhu Mao
https://doi.org/10.13052/97887-438-0157-3_13


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Article 14: A Generative Modeling Approach for the Translation of Operational Variables to Short-term Vibrations
by Arthur Hatstatt, Konstantinos E. Tatsis
https://doi.org/10.13052/97887-438-0157-3_14


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Article 15: Effective Structural Health Monitoring of Rotating Propellers using Asynchronous Neuromorphic Tracking
by Guillermo Toledo, Wyatt Saeger, Fernando Moreu, David Mascarenas, Christian Torres, Jahsyel Rojas
https://doi.org/10.13052/97887-438-0157-3_15


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Article 16: Estimating Damage Detection of an Aircraft Component with Machine Learning Models
by Brandon Jones, Milton Jones, Stephen Keyes, Eamon Mott, Thomas J. Matarazzo
https://doi.org/10.13052/97887-438-0157-3_16


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Article 17: Physics-Informed Machine Learning for Advanced Structural Damage Detection and Localization
by Zixin Wang, Mohammad R. Jahanshahi, and Shirley J. Dyke
https://doi.org/10.13052/97887-438-0157-3_17


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Article 18: Damage Detection Strategy Based on PCA/Mode-Shapes Developed on a Laboratory Truss Girder Subjected to Environmental Variations
by M. Berardengo, F. Luca, S. Manzoni, S. Pavoni, M. Vanali
https://doi.org/10.13052/97887-438-0157-3_18


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