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Data Science in Engineering, Volume 9

Proceedings of the 39th IMAC, A Conference and Exposition on Structural Dynamics 2021

  • Conference proceedings
  • © 2022

Overview

Editors:
  1. Ramin Madarshahian

    1. University of California, San Diego, San Diego, USA

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    You can also search for this editor in PubMed   Google Scholar

  2. Francois Hemez

    1. Department of Energy-Defense Programs, Lawrence Livermore National Laboratory, Livermore, USA

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    You can also search for this editor in PubMed   Google Scholar

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Table of contents (33 papers)

  1. Front Matter

    Pages i-viii
    Download chapter PDF

  2. Towards Population-Based Structural Health Monitoring, Part V: Networks and Databases

    • Chandula T. Wickramarachchi, Daniel S. Brennan, Weijiang Lin, Eoghan Maguire, Dustin Y. Harvey, Elizabeth J. Cross et al.
    Pages 1-8

  3. Active Learning of Post-earthquake Structural Damage with Co-optimal Information Gain and Reconnaissance Cost

    • Mohamadreza Sheibani, Ge Ou
    Pages 9-16

  4. Uncertainty-Quantified Damage Identification for High-Rate Dynamic Systems

    • Zihan Wu, Michael D. Todd
    Pages 17-20

  5. Real-Time Forecasting of Vibrations with Non-stationarities

    • Ishrat Singh, Philip Conrad, Puja Chowdhury, Jason D. Bakos, Austin Downey
    Pages 21-29

  6. Data-Driven Identification of Mistuning in Blisks

    • Sean T. Kelly, Andrea Lupini, Bogdan I. Epureanu
    Pages 31-34

  7. On Generating Parametrised Structural Data Using Conditional Generative Adversarial Networks

    • G. Tsialiamanis, D. J. Wagg, N. Dervilis, K. Worden
    Pages 35-46

  8. On an Application of Graph Neural Networks in Population-Based SHM

    • G. Tsialiamanis, C. Mylonas, E. N. Chatzi, D. J. Wagg, N. Dervilis, K. Worden
    Pages 47-63

  9. Estimation of Elastic Band Gaps Using Data-Driven Modeling

    • H. Gosavi, Vijaya V. N. Sriram Malladi
    Pages 65-69

  10. Damage Localization on Lightweight Structures with Non-destructive Testing and Machine Learning Techniques

    • A. Tavares, E. Di Lorenzo, B. Cornelis, B. Peeters, K. Gryllias, W. Desmet
    Pages 71-74

  11. Challenges for SHM from Structural Repairs: An Outlier-Informed Domain Adaptation Approach

    • P. A. Gardner, L. A. Bull, N. Dervilis, K. Worden
    Pages 75-86

  12. On the Application of Heterogeneous Transfer Learning to Population-Based Structural Health Monitoring

    • P. A. Gardner, L. A. Bull, N. Dervilis, K. Worden
    Pages 87-98

  13. An Unsupervised Deep Auto-encoder with One-Class Support Vector Machine for Damage Detection

    • Zilong Wang, Young-Jin Cha
    Pages 99-104

  14. Identifying Environmental- and Operational-Insensitive Damage Features

    • David Gibbs, Kaleb Jankowski, Ben Rees, Charles Farrar, Garrison Flynn
    Pages 105-121

  15. Hybrid Concrete Crack Segmentation and Quantification Across Complex Backgrounds Without a Large Training Dataset

    • Dong Ho Kang, Sukhpreet Benipal, Young-Jin Cha
    Pages 123-128

  16. Digital Stroboscopy Using Event-Driven Imagery

    • Andrew Gothard, Corey Henry, Daniel Jones, Alessandro Cattaneo, David Mascareñas
    Pages 129-133

  17. Parameter Estimation for Dynamical Systems Under Continuous and Discontinuous Gaussian Noise Using Data Assimilation Techniques

    • Justin Jacobs, David Najera-Flores, Adam R. Brink, Tatiana Flanagan
    Pages 135-143

  18. Model Reduction of Geometrically Nonlinear Structures ViaPhysics-Informed Autoencoders

    • David A. Najera, Christopher I. Van Damme, Victoria Harris
    Pages 145-148

  19. Techniques to Improve Robustness of Video-Based Sensor Networks

    • Sarah Mantell, Nathan Sapong, Addison Schwamb, David Mascareñas, Alessandro Cattaneo
    Pages 149-160

  20. Grey-Box Modelling via Gaussian Process Mean Functions for Mechanical Systems

    • Sikai Zhang, Elizabeth J. Cross
    Pages 161-168
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Keywords

About this book

Data Science and Engineering Volume 9: Proceedings of the 39th IMAC, A Conference and Exposition on Structural Dynamics, 2021, the ninth volume of nine 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:

Data Science in Engineering Applications

Engineering Mathematics

Computational Methods in Engineering


Editors and Affiliations

  • University of California, San Diego, San Diego, USA

    Ramin Madarshahian

  • Department of Energy-Defense Programs, Lawrence Livermore National Laboratory, Livermore, USA

    Francois Hemez

About the editors

Ramin Madarshahian, University of San Diego, CA, USA; Francois Hemez, Lawrence Livermore National Laboratory, Livermore, CA, USA.

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