Data Science in Engineering Vol. 10
Proceedings of the 42nd IMAC, A Conference and Exposition on Structural Dynamics 2024
1st Edition
Published on 16. October 2024
X, 143 pages
978-3-031-68142-4 (ISBN)
Description
Data Science in Engineering, Volume 10: Proceedings of the 42 nd IMAC, A Conference and Exposition on Structural Dynamics, 2024, the tenth volume of ten 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 Data Science in Engineering, including papers on:
- Novel Data-driven Analysis Methods
- Deep Learning Gaussian Process Analysis
- Real-time Video-based Analysis
- Applications to Nonlinear Dynamics and Damage Detection
- Data-driven System Prognostics
More details
Series
Language
English
Place of publication
Cham
Switzerland
Publishing group
Springer International Publishing
Target group
Professional and scholarly
Product notice
Reflowable
Illustrations
X, 143 p. 93 illus., 79 illus. in color.
File size
18,72 MB
ISBN-13
978-3-031-68142-4 (9783031681424)
DOI
10.1007/978-3-031-68142-4
Schweitzer Classification
Other editions
Additional editions
Thomas Matarazzo | François Hemez | Eleonora Maria Tronci
Data Science in Engineering Vol. 10
Proceedings of the 42nd IMAC, A Conference and Exposition on Structural Dynamics 2024
Book
10/2024
Springer
€181.89
Shipment within 15-20 days
Persons
Content
- 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
