Data Science in Engineering Vol. 10

Name: Data Science in Engineering Vol. 10 | Proceedings of the 42nd IMAC, A Conference and Exposition on Structural Dynamics 2024
Brand: Springer
Price: 171.19 EUR
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Proceedings of the 42nd IMAC, A Conference and Exposition on Structural Dynamics 2024

Thomas Matarazzo François Hemez Eleonora Maria Tronci Austin Downey(Herausgeber*in)

Springer (Verlag)

1. Auflage

Erschienen am 16. Oktober 2024

X, 143 Seiten

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978-3-031-68142-4 (ISBN)

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Inhalt

Intro
Preface
Contents
Physics-Informed Machine Learning Part I: Different Strategies to Incorporate Physics into Engineering Problems
Introduction
Integrating First-Principle and Data-Driven Machine Learning Models
Challenges and Open Research Questions
References
Statistical Evaluation of Machine Learning for Vibration Data
Introduction
Background
Data
Model and Training
Evaluation
Results
Discussion and Conclusion
References
Quantifying the Value of Information Transfer in Population-Based SHM
Introduction
Background
Population-Based SHM
Transfer Learning for PBSHM
Negative Transfer
Risk-Informed Transfer Learning
Effects of Negative Transfer on Decision-Making
Transfer Strategy Optimisation
Case Study: 10-DoF Systems
Classification Task
Transfer Tasks
Structural Similarity
Forecasting Prediction Quality
Results
Discussion
Conclusions
References
Employing Guided Wave-Based Damage Localization Techniques for Additively Manufactured Plates with Different Infill Densities
Introduction
Experimental Setup
Damage Localization Approach
Results
Conclusions
References
Optimal Modeling of Deep Groove Ball Bearings for Application in Multibody Dynamics Simulations
Introduction
Background
Analysis
Conclusion
References
Utilization of Bridge Acceleration Response for Indirect Strain Sensing
Introduction
Methodology
Analysis and Results
Conclusion
References
Transfer Learning Across Heterogeneous Structures Through Adversarial Training
Introduction
Datasets
Methodology
Damage Detection Model
Domain Adaptation Module
Experiments and Results
Conclusions
References
Physics-Informed Machine Learning Part II: Applications in Structural Response Forecasting
Introduction
Methodology
Example
Conclusion
References
Frequency-Based Damage Detection Using Drone-deployable Sensor Package with Edge Computing
Introduction
Sensor Design
Edge Computing Algorithm
Testing Procedure
Results and Discussion
Conclusion
References
Understanding High-Frequency Modes in Electromechanical Impedance Measurement Using Noncontact Vibration Testing
Introduction
Electromechanical Impedance
Methodology and Experimental Setup
Results and Discussions
Conclusion and Future Works
Appendix
References
On the Use of Symbolic Regression for Population-Based Modelling of Structures
Introduction
Population-Based Structural Health Monitoring
Symbolic Regression
Application
Conclusions and Next Steps
References
Markov Chain Monte Carlo on Matrix Manifolds for Probabilistic Model Order Reduction
Introduction
Background
Analysis
Conclusion
References
Identification of Bird Species in Large Multi-channel Data Streams Using Distributed Acoustic Sensing
Introduction
Birds as Indicators of Ecological Health
DAS
Bird Sound Processing
Methods
Results
Detection and Classification
Conclusion
References
A Machine Learning -Based Damage Estimation Model for Monitoring Reinforced Concrete Structures
Introduction
Method
Experimental Procedure
Modeling
Results and Discussion
Conclusion
References
Adaptive Radio Frequency Target Localization
Introduction
Background
Methods
Simulation to Real-World Gap
Experiments
Results
Conclusion and Future Work
References
Estimation of Acoustic Emission Arrival Time in Concrete Structures Using Convolutional Neural Network
Introduction
Methodology
Overview of Time of Arrival (ToA)
One-Dimensional Convolutional Neural Network (1D CNN)
Experimental Setup
Modeling
Results and Discussion
Conclusion
References
Machine Learning -Based Method for Structural Damage Detection
Introduction
Background
Analysis
Isotropic Plate
Orthotropic Plate
Conclusion
References
Correction to: Markov Chain Monte Carlo on Matrix Manifolds for Probabilistic Model Order Reduction
Correction to: Markov Chain Monte Carlo on Matrix Manifolds for Probabilistic Model Order Reduction
Correction to:Chapter 12 in: T. Matarazzo et al. (eds.), Data Science in Engineering Vol. 10, Conference Proceedings of the Society for Experimental Mechanics Serieshttps://doi.org/10.1007/978-3-031-68142-4_12

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