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Structural Health Monitoring Based on Data Science Techniques

  • Book
  • © 2022

Overview

Editors:
  1. Alexandre Cury

    1. Department of Applied and Computational Mechanics, Federal University of Juiz de Fora, Juiz de Fora, Brazil

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  2. Diogo Ribeiro

    1. Department of Civil Engineering, School of Engineering, Polytechnic Institute of Porto, Porto, Portugal

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  3. Filippo Ubertini

    1. Department of Civil and Environmental Engineering, University of Perugia, Perugia, Italy

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  4. Michael D. Todd

    1. Structural Engineering, University of California San Diego, La Jolla, USA

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  • Presents a collection of data science applied to structural health monitoring applications
  • Includes experimental and field examples of detection and identification approaches
  • Explains how data can be used in the decision-making process of structural maintenance and usage

Part of the book series: Structural Integrity (STIN, volume 21)

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Table of contents (22 chapters)

  1. Front Matter

    Pages i-xv
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  2. Vibration-Based Structural Damage Detection Using Sparse Bayesian Learning Techniques

    • Rongrong Hou, Xiaoyou Wang, Yong Xia
    Pages 1-25

  3. Bayesian Deep Learning for Vibration-Based Bridge Damage Detection

    • Davíð Steinar Ásgrímsson, Ignacio González, Giampiero Salvi, Raid Karoumi
    Pages 27-43

  4. Diagnosis, Prognosis, and Maintenance Decision Making for Civil Infrastructure: Bayesian Data Analytics and Machine Learning

    • Manuel A. Vega, Zhen Hu, Yichao Yang, Mayank Chadha, Michael D. Todd
    Pages 45-73

  5. Real-Time Machine Learning for High-Rate Structural Health Monitoring

    • Simon Laflamme, Chao Hu, Jacob Dodson
    Pages 75-94

  6. Development and Validation of a Data-Based SHM Method for Railway Bridges

    • Ana Cláudia Neves, Ignacio González, Raid Karoumi
    Pages 95-116

  7. Real-Time Unsupervised Detection of Early Damage in Railway Bridges Using Traffic-Induced Responses

    • Andreia Meixedo, Diogo Ribeiro, João Santos, Rui Calçada, Michael D. Todd
    Pages 117-142

  8. Fault Diagnosis in Structural Health Monitoring Systems Using Signal Processing and Machine Learning Techniques

    • Henrieke Fritz, José Joaquín Peralta Abadía, Dmitrii Legatiuk, Maria Steiner, Kosmas Dragos, Kay Smarsly
    Pages 143-164

  9. A Self-adaptive Hybrid Model/data-Driven Approach to SHM Based on Model Order Reduction and Deep Learning

    • Luca Rosafalco, Matteo Torzoni, Andrea Manzoni, Stefano Mariani, Alberto Corigliano
    Pages 165-184

  10. Predictive Monitoring of Large-Scale Engineering Assets Using Machine Learning Techniques and Reduced-Order Modeling

    • Caterina Bigoni, Mengwu Guo, Jan S. Hesthaven
    Pages 185-205

  11. Unsupervised Data-Driven Methods for Damage Identification in Discontinuous Media

    • Rebecca Napolitano, Wesley Reinhart, Branko Glisic
    Pages 207-226

  12. Applications of Deep Learning in Intelligent Construction

    • Yang Zhang, Ka-Veng Yuen
    Pages 227-245

  13. Integrated SHM Systems: Damage Detection Through Unsupervised Learning and Data Fusion

    • Enrique García-Macías, Filippo Ubertini
    Pages 247-268

  14. Environmental Influence on Modal Parameters: Linear and Nonlinear Methods for Its Compensation in the Context of Structural Health Monitoring

    • Carlo Rainieri
    Pages 269-288

  15. Vibration-Based Damage Feature for Long-Term Structural Health Monitoring Under Realistic Environmental and Operational Variability

    • Francescantonio Lucà, Stefano Manzoni, Alfredo Cigada
    Pages 289-307

  16. On Explicit and Implicit Procedures to Mitigate Environmental and Operational Variabilities in Data-Driven Structural Health Monitoring

    • David García Cava, Luis David Avendaño-Valencia, Artur Movsessian, Callum Roberts, Dmitri Tcherniak
    Pages 309-330

  17. Explainable Artificial Intelligence to Advance Structural Health Monitoring

    • Daniel Luckey, Henrieke Fritz, Dmitrii Legatiuk, José Joaquín Peralta Abadía, Christian Walther, Kay Smarsly
    Pages 331-346

  18. Physics-Informed Machine Learning for Structural Health Monitoring

    • Elizabeth J. Cross, S. J. Gibson, M. R. Jones, D. J. Pitchforth, S. Zhang, T. J. Rogers
    Pages 347-367

  19. Interpretable Machine Learning for Function Approximation in Structural Health Monitoring

    • Jin-Song Pei, Dean F. Hougen, Sai Teja Kanneganti, Joseph P. Wright, Eric C. Mai, Andrew W. Smyth et al.
    Pages 369-388

  20. Partially Supervised Learning for Data-Driven Structural Health Monitoring

    • Lawrence A. Bull, A. J. Hughes, T. J. Rogers, Paul Gardner, Keith Worden, Nikolaos Dervilis
    Pages 389-411
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Keywords

About this book



The modern structural health monitoring (SHM) paradigm of transforming in situ, real-time data acquisition into actionable decisions regarding structural performance, health state, maintenance, or life cycle assessment has been accelerated by the rapid growth of “big data” availability and advanced data science. Such data availability coupled with a wide variety of machine learning and data analytics techniques have led to rapid advancement of how SHM is executed, enabling increased transformation from research to practice. This book intends to present a representative collection of such data science advancements used for SHM applications, providing an important contribution for civil engineers, researchers, and practitioners around the world.

Editors and Affiliations

  • Department of Applied and Computational Mechanics, Federal University of Juiz de Fora, Juiz de Fora, Brazil

    Alexandre Cury

  • Department of Civil Engineering, School of Engineering, Polytechnic Institute of Porto, Porto, Portugal

    Diogo Ribeiro

  • Department of Civil and Environmental Engineering, University of Perugia, Perugia, Italy

    Filippo Ubertini

  • Structural Engineering, University of California San Diego, La Jolla, USA

    Michael D. Todd

Bibliographic Information

  • Book Title: Structural Health Monitoring Based on Data Science Techniques

  • Editors: Alexandre Cury, Diogo Ribeiro, Filippo Ubertini, Michael D. Todd

  • Series Title: Structural Integrity

  • DOI: https://doi.org/10.1007/978-3-030-81716-9

  • Publisher: Springer Cham

  • eBook Packages: Engineering, Engineering (R0)

  • Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022

  • Hardcover ISBN: 978-3-030-81715-2Published: 24 October 2021

  • Softcover ISBN: 978-3-030-81718-3Published: 25 October 2022

  • eBook ISBN: 978-3-030-81716-9Published: 23 October 2021

  • Series ISSN: 2522-560X

  • Series E-ISSN: 2522-5618

  • Edition Number: 1

  • Number of Pages: XV, 484

  • Number of Illustrations: 45 b/w illustrations, 268 illustrations in colour

  • Topics: Data Structures and Information Theory, Artificial Intelligence, Machine Learning, Statistics, general

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