Data Science in Engineering, Volume 10: Proceedings of the 41st IMAC, A Conference and Exposition on Structural Dynamics, 2023, 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
- High-rate Structural Monitoring and Prognostics
Chapter
1. A Meta-Learning Approach to Population-Based Modelling of
Structure.-,
Chapter 2 State Space Reconstruction from Embeddings of Partial
Observables in Structural Dynamic Systems for Structure-Preserving
Data-Driven Methods.-,
Chapter 3 Chapter
2. State Space Reconstruction from
Embeddings of Partial Observables in Structural Dynamic Systems for
Structure-Preserving Data-Driven Methods.-,
Chapter 4 Composite Neural
Network Framework for Modeling Impulsive Nonlinear Dynamic Responses.-,
Chapter 5 Towards physics-based metrics for transfer learning in dynamics.-,
Chapter 6 Principal Component Analysis of Monitoring Data of a High-Rise
Building: The Case Study of Palazzo Lombardia.-,
Chapter 7 Optimal
Contact-Impact Force Model Selection for Damage Detection in Ball Bearings.-,
Chapter 8 Simulation Error Influence on Damage Identification Classifiers
Trained by Numerical Data.-,
Chapter 9 Structural Health Monitoring in the
Context ofNon-Equilibrium Phase Transitions.-,
Chapter 10 Synthetic Thermal
Image Data Generation using Attention-Based Generative Adversarial Network
for Concrete Internal Damage Segmentation.-,
Chapter 11 Optimal Fiber Optic
Sensor Placement Framework for Structural Health Monitoring of an Aircrafts
Wing Spar.-,
Chapter 12 Construction Noise Cancellation with Feedback Active
Control using Machine Learning.-,
Chapter 13 Physics-Informed Data-Driven
Reduced-Order Model for Turbomachinery Blisk.-,
Chapter 14 High-rate
Structural Health Monitoring: Part-II Embedded System Design.-,
Chapter
15 Damage Quantification under High-Rate Dynamic Loading and Data
Augmentation using Generative Adversarial Network.-,
Chapter16 Output-only
versus Direct Input-output Structural Condition Monitoring Methods.-,
Chapter
17 High-rate Structural Health Monitoring: Part-III Algorithm.-,
Chapter 18 A
population form via hierarchical Bayesian modelling of the FRF.-,
Chapter
19 Lupos: Open-source Scientific Computing in Structural Dynamics.-,
Chapter
20 Expert Knowledge-Driven Condition Assessment of Railway Welds from Axle
Box Accelerations using Random Forests and Bayesian Logistic Regression.-,
Chapter 21 On quantifying data normalisation via cointegration with
topological methods.-,
Chapter 22 Automatic Selection of Optimal Structures
for Population-based Structural Health Monitoring.-,
Chapter 23 Online
back-propagation of recurrent neural network for forecasting nonstationary
structural responses.
Ramin MadarshahianCompany: Kount, an Equifax company, Boise, ID, USA ;Francois HemezLawrence Livermore National Laboratory, Livermore, CA, USA
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