Data Science in Engineering, Volume 10

Proceedings of the 41st IMAC, A Conference and Exposition on Structural Dynamics 2023

Ramin Madarshahian, Fran�s Hemez

• Publisher: River Publishers
• Year: 2025
• eBook ISBN:
  9781040602003
• Topic: Computer Science
• Index: Computer Science

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.

Table of contents

• Cover
• Series Page
• Title Page
• Copyright Page
• Preface
• Table of Contents
• Chapter 1 Output-Only Versus Direct Input-Output Structural Condition Monitoring Methods
• Chapter 2 Construction Noise Cancellation with Feedback Active Control Using Machine Learning
• Chapter 3 Simulation Error Influence on Damage Identification Classifiers Trained by Numerical Data
• Chapter 4 Synthetic Thermal Image Data Generation Using Attention-Based Generative Adversarial Network for Concrete Internal Damage Segmentation
• Chapter 5 Principal Component Analysis of Monitoring Data of a High-Rise Building: The Case Study of Palazzo Lombardia
• Chapter 6 On Quantifying Data Normalisation via Cointegration with Topological Methods
• Chapter 7 Better Together: Using Multi-Task Learning to Improve Feature Selection Within Structural Datasets
• Chapter 8 A Meta-Learning Approach to Population-Based Modelling of Structures
• Chapter 9 Towards Physics-Based Metrics for Transfer Learning in Dynamics
• Chapter 10 Automatic Selection of Optimal Structures for Population-Based Structural Health Monitoring
• Chapter 11 A Population Form via Hierarchical Bayesian Modelling of the FRF
• Chapter 12 High-Rate Structural Health Monitoring: Part-II Embedded System Design
• Chapter 13 High-Rate Structural Health Monitoring: Part-III Algorithms
• Chapter 14 Damage Quantification Under High-Rate Dynamic Loading and Data Augmentation Using Generative Adversarial Network
• Chapter 15 Optimal Contact-Impact Force Model Selection for Damage Detection in Ball Bearings
• Chapter 16 Optimal Fiber-Optic Sensor Placement Framework for Structural Health Monitoring of an Aircraft’s Wing Spar
• Chapter 17 Online Backpropagation of Recurrent Neural Network for Forecasting Nonstationary Structural Responses
• Chapter 18 State Space Reconstruction from Embeddings of Partial Observables in Structural Dynamic Systems for Structure-Preserving Data-Driven Methods
• Chapter 19 Physics-Informed Data-Driven Reduced-Order Model for Turbomachinery Blisks
• Chapter 20 Structural Health Monitoring in the Context of Nonequilibrium Phase Transitions
• Chapter 21 Composite Neural Network Framework for Modeling Impulsive Nonlinear Dynamic Responses
• Chapter 22 Expert Knowledge-Driven Condition Assessment of Railway Welds from Axle Box Accelerations Using Random Forests and Bayesian Logistic Regression
• Chapter 23 LUPOS: Open-Source Scientific Computing in Structural Dynamics
• Correction to: LUPOS: Open-Source Scientific Computing in Structural Dynamics