Structural Health Monitoring & Machine Learning, Vol. 12

Proceedings of the 43rd IMAC, A Conference and Exposition on Structural Dynamics 2025

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

• Publisher: River Publishers
• Year: 2026
• eBook ISBN:
  9788743807681
• Topic: Technology & Engineering
• Index: Technology & Engineering

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

Table of contents

• Cover Page
• Series Page
• Title Page
• Copyright Page
• Preface
• Contents
• Chapter 1 Theoretical Foundations and Practical Applications of Damage Detection Using Autocovariance Functions
• Chapter 2 On the Real Time Tightness Measurement of Complex Shaped Flanges
• Chapter 3 Parameter Rejection in Sensitivity-based Model Updating using Output Feedback Eigenstructure Assignment
• Chapter 4 Structural Health Monitoring of a Ferry Quay: Instrumentation and Impact of Tidal Levels on Modal Parameters
• Chapter 5 Outcomes from Field Measurements on the Magerholm Ferry Quay: System Identification, Finite Element Model Updating and Sensitivity Analysis
• Chapter 6 A Robust Data-Driven Algorithm for Early Damage Detection in Structural Health Monitoring
• Chapter 7 Real-Time Structural Health Assessment of a Tension Rod Assembly Using Machine Learning
• Chapter 8 Multi-Bridge Indirect Structural Health Monitoring: Leveraging Big Data and Drive-By Crowdsensing Techniques
• Chapter 9 A Comparative Study of Feature Selection Methods for Wind Turbine Gearbox Bearing Fault Prognosis
• Chapter 10 Damage Identification on Gear Drivetrains Using Neural Networks Trained by High-Fidelity Multibody Simulation Data
• Chapter 11 Advanced Condition Monitoring framework for CFRP Gear Drivetrains Using Machine Learning and Multibody Dynamics Simulations
• Chapter 12 On the use of Statistical Learning Theory for model selection in Structural Health Monitoring
• Chapter 13 Full-field Measurements for Anomaly Detection of Mechanical Systems using Convolutional Neural Networks and LSTM Networks
• Chapter 14 A Generative Modeling Approach for the Translation of Operational Variables to Short-term Vibrations
• Chapter 15 Effective Structural Health Monitoring of Rotating Propellers using Asynchronous Neuromorphic Tracking
• Chapter 16 Estimating Damage Detection of an Aircraft Component with Machine Learning Models
• Chapter 17 Physics-Informed Machine Learning for Advanced Structural Damage Detection and Localization
• Chapter 18 Damage Detection Strategy Based on PCA/Mode-Shapes Developed on a Laboratory Truss Girder Subjected to Environmental Variations