Data Science in Engineering Vol. 10: Proceedings of the 42nd IMAC, A Conference and Exposition on Structural Dynamics 2024

by Thomas Matarazzo (Editor), Fran?s Hemez (Editor), Eleonora Maria Tronci (Editor), Austin Downey (Editor)

Overview

Data Science in Engineering, Volume 10: Proceedings of the 42nd 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.

Product Details

ISBN-13:	9781040601914
Publisher:	River Publishers
Publication date:	08/07/2025
Sold by:	Barnes & Noble
Format:	eBook
Pages:	141
File size:	16 MB
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About the Author

Thomas Matarazzo, United States Military Academy, West Point, USA. François Hemez, Department of Energy-Defense Programs, Lawrence Livermore National Laboratory, Livermore, USA. Eleonora Maria Tronci, Northeastern University, Boston, USA. Austin Downey, University of South Carolina, Columbia, USA.

Table of Contents

Preface, Physics-Informed Machine Learning Part I: Different Strategies to Incorporate Physics into Engineering Problems; Statistical Evaluation of Machine Learning for Vibration Data; Quantifying the Value of Information Transfer in Population-Based SHM; Employing Guided Wave-Based Damage Localization Techniques for Additively Manufactured Plates with Different Infill Densities; Optimal Modeling of Deep Groove Ball Bearings for Application in Multibody Dynamics Simulations; Utilization of Bridge Acceleration Response for Indirect Strain Sensing; Transfer Learning Across Heterogeneous Structures Through Adversarial Training; Physics-Informed Machine Learning Part II: Applications in Structural Response Forecasting; Frequency-Based Damage Detection Using Drone-deployable Sensor Package with Edge Computing; Understanding High-Frequency Modes in Electromechanical Impedance Measurement Using Noncontact Vibration Testing; On the Use of Symbolic Regression for Population-Based Modelling of Structures; Markov Chain Monte Carlo on Matrix Manifolds for Probabilistic Model Order Reduction; Identification of Bird Species in Large Multi-channel Data Streams Using Distributed Acoustic Sensing ; A Machine Learning –Based Damage Estimation Model for Monitoring Reinforced Concrete Structures; Adaptive Radio Frequency Target Localization; Estimation of Acoustic Emission Arrival Time in Concrete Structures Using Convolutional Neural Network; Machine Learning –Based Method for Structural Damage Detection; Correction to: Markov Chain Monte Carlo on Matrix Manifolds for Probabilistic Model Order Reduction.