rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Structural Health Monitoring Based on Data Science Techniques
  name:
    name part: Cury, Alexandre.
    role:
      role term: editor.
    role:
      role term: edt
    role:
      role term: http://id.loc.gov/vocabulary/relators/edt
  name:
    name part: Ribeiro, Diogo.
    role:
      role term: editor.
    role:
      role term: edt
    role:
      role term: http://id.loc.gov/vocabulary/relators/edt
  name:
    name part: Ubertini, Filippo.
    role:
      role term: editor.
    role:
      role term: edt
    role:
      role term: http://id.loc.gov/vocabulary/relators/edt
  name:
    name part: Todd, Michael D.
    role:
      role term: editor.
    role:
      role term: edt
    role:
      role term: http://id.loc.gov/vocabulary/relators/edt
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  genre: Electronic books.
  origin info:
    place:
      place term: sz
    date issued: 2022
    edition: 1st ed. 2022.
    issuance: monographic
  language:
    language term: eng
  physical description:
    reformatting quality: access
    form: remote
    extent: 1 online resource (XV, 484 pages 313 illustrations, 268 illustrations in color.)
  abstract: 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.
  table of contents: Chapter 1. Vibration-based structural damage detection using sparse Bayesian learning techniques (Rongrong Hou) -- Chapter 2. Bayesian deep learning for vibration-based bridge damage detection (Davíð Steinar Ásgrímsson) -- Chapter 3. Diagnosis, Prognosis, and Maintenance Decision Making for Civil Infrastructure: Bayesian Data Analytics and Machine Learning (Manuel A. Vega) -- Chapter 4. Real-Time Machine Learning for High-Rate Structural Health Monitoring (Simon Laflamme) -- Chapter 5. Development and validation of a data-based SHM method for railway bridges (Ana Claudia Neves) -- Chapter 6. Real-time unsupervised detection of early damage in railway bridges using traffic-induced responses (Andreia Meixedo) -- Chapter 7. Fault diagnosis in structural health monitoring systems using signal processing and machine learning techniques (Henrieke Fritz). Chapter 8. A self-adaptive hybrid model/data-driven approach to SHM based on Model Order Reduction and Deep Learning (Luca Rosafalco) -- Chapter 9. Predictive monitoring of large-scale engineering assets using machine learning techniques and reduced order modeling (Caterina Bigoni) -- Chapter 10. Unsupervised data-driven methods for damage identification in discontinuous media (Rebecca Napolitano) -- Chapter 11. Applications of Deep Learning in intelligent construction (Yang Zhang) -- Chapter 12. Integrated SHM systems: Damage detection through unsupervised learning and data fusion (Enrique García-Macías) -- Chapter 13. Environmental influence on modal parameters: linear and non-linear methods for its compensation in the context of Structural Health Monitoring (Carlo Rainieri) -- Chapter 14. Vibration based damage feature for long-term structural health monitoring under realistic environmental and operational variability (Francescantonio Lucà) -- Chapter 15. On explicit and implicit procedures to mitigate environmental and operational variabilities in data-driven structural health monitoring (David García Cava). Chapter 1…
  note: edited by Alexandre Cury, Diogo Ribeiro, Filippo Ubertini, Michael D. Todd.
  subject:
    topic: Data structures (Computer science)
  subject:
    topic: Artificial intelligence
  subject:
    topic: Machine learning
  subject:
    topic: Statistics
  subject:
    topic: Data Structures and Information Theory
  subject:
    topic: Artificial Intelligence
  subject:
    topic: Machine Learning
  subject:
    topic: Statistics, general
  classification: QA76.9.D35
  classification: Q350-390
  classification: 005.73
  related item:
    title info:
      title: Springer Nature eBook
  related item:
    title info:
      title: Structural Integrity, 21
  identifier: 9783030817169
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record creation date: 211023
    record change date: 20240805193134.2
    record identifier: in00004406709