Data Science in Engineering, Volume 10
Proceedings of the 41st IMAC, A Conference and Exposition on Structural Dynamics 2023
Published on 7. August 2025
185 pages
978-1-040-60200-3 (ISBN)
Description
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.
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.
More details
Edition
Digital original
Language
English
Place of publication
London
United Kingdom
Edition type
Digital original
File size
21,29 MB
ISBN-13
978-1-040-60200-3 (9781040602003)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Persons
Ramin Madarshahian and Francois Hemez both from Kount, an Equifax company, Boise, USA.
Content
Preface, 1 Output-Only Versus Direct Input-Output Structural Condition Monitoring Methods, 2 Construction Noise Cancellation with Feedback Active Control Using Machine Learning, 3 Simulation Error Influence on Damage Identification Classifiers Trained by Numerical Data, 4 Synthetic Thermal Image Data Generation Using Attention-Based Generative Adversarial Network for Concrete Internal Damage Segmentation, 5 Principal Component Analysis of Monitoring Data of a High-Rise Building: The Case Study of Palazzo Lombardia, 6 On Quantifying Data Normalisation via Cointegration with Topological Methods, 7 Better Together: Using Multi-Task Learning to Improve Feature Selection Within Structural Datasets, 8 A Meta-Learning Approach to Population-Based Modelling of Structures, 9 Towards Physics-Based Metrics for Transfer Learning in Dynamics, 10 Automatic Selection of Optimal Structures for Population-Based Structural Health Monitoring, 11 A Population Form via Hierarchical Bayesian Modelling of the FRF, 12 High-Rate Structural Health Monitoring: Part-II Embedded System Design, 13 High-Rate Structural Health Monitoring: Part-III Algorithms, 14 Damage Quantification Under High-Rate Dynamic Loading and Data Augmentation Using Generative Adversarial Network, 15 Optimal Contact-Impact Force Model Selection for Damage Detection in Ball Bearings, 16 Optimal Fiber-Optic Sensor Placement Framework for Structural Health Monitoring of an Aircraft's Wing Spar, 17 Online Backpropagation of Recurrent Neural Network for Forecasting Nonstationary Structural Responses, 18 State Space Reconstruction from Embeddings of Partial Observables in Structural Dynamic Systems for Structure-Preserving Data-Driven Methods, 19 Physics-Informed Data-Driven Reduced-Order Model for Turbomachinery Blisks, 20 Structural Health Monitoring in the Context of Nonequilibrium Phase Transitions, 21 Composite Neural Network Framework for Modeling Impulsive Nonlinear Dynamic Responses, 22 Expert Knowledge-Driven Condition Assessment of Railway Welds from Axle Box Accelerations Using Random Forests and Bayesian Logistic Regression, 23 LUPOS: Open-Source Scientific Computing in Structural Dynamics, Correction to: LUPOS: Open-Source Scientific Computing in Structural Dynamics.
