Data Driven Methods for Civil Structural Health Monitoring and Resilience : Latest Developments and Applications

Bibliographic Details
Title: Data Driven Methods for Civil Structural Health Monitoring and Resilience : Latest Developments and Applications
Description: Data Driven Methods for Civil Structural Health Monitoring and Resilience: Latest Developments and Applications provides a comprehensive overview of data-driven methods for structural health monitoring (SHM) and resilience of civil engineering structures, mostly based on artificial intelligence or other advanced data science techniques. This allows existing structures to be turned into smart structures, thereby allowing them to provide intelligible information about their state of health and performance on a continuous, relatively real-time basis. Artificial-intelligence-based methodologies are becoming increasingly more attractive for civil engineering and SHM applications; machine learning and deep learning methods can be applied and further developed to transform the available data into valuable information for engineers and decision makers.
Authors: Mohammad Noori, Carlo Rainieri, Marco Domaneschi, Vasilis Sarhosis, Wael A. Altabey
Resource Type: eBook.
Subjects: Structural analysis (Engineering)--Data processing
Categories: TECHNOLOGY & ENGINEERING / Structural, TECHNOLOGY & ENGINEERING / Sensors, COMPUTERS / Data Science / Machine Learning
Database: eBook Index
FullText Text:
  Availability: 0
Header DbId: edsebk
DbLabel: eBook Index
An: 3678212
RelevancyScore: 969
AccessLevel: 6
PubType: eBook
PubTypeId: ebook
PreciseRelevancyScore: 968.509704589844
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Data Driven Methods for Civil Structural Health Monitoring and Resilience : Latest Developments and Applications
– Name: Abstract
  Label: Description
  Group: Ab
  Data: Data Driven Methods for Civil Structural Health Monitoring and Resilience: Latest Developments and Applications provides a comprehensive overview of data-driven methods for structural health monitoring (SHM) and resilience of civil engineering structures, mostly based on artificial intelligence or other advanced data science techniques. This allows existing structures to be turned into smart structures, thereby allowing them to provide intelligible information about their state of health and performance on a continuous, relatively real-time basis. Artificial-intelligence-based methodologies are becoming increasingly more attractive for civil engineering and SHM applications; machine learning and deep learning methods can be applied and further developed to transform the available data into valuable information for engineers and decision makers.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Mohammad+Noori%22">Mohammad Noori</searchLink><br /><searchLink fieldCode="AR" term="%22Carlo+Rainieri%22">Carlo Rainieri</searchLink><br /><searchLink fieldCode="AR" term="%22Marco+Domaneschi%22">Marco Domaneschi</searchLink><br /><searchLink fieldCode="AR" term="%22Vasilis+Sarhosis%22">Vasilis Sarhosis</searchLink><br /><searchLink fieldCode="AR" term="%22Wael+A%2E+Altabey%22">Wael A. Altabey</searchLink>
– Name: TypePub
  Label: Resource Type
  Group: TypPub
  Data: eBook.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Structural+analysis+%28Engineering%29--Data+processing%22">Structural analysis (Engineering)--Data processing</searchLink>
– Name: SubjectBISAC
  Label: Categories
  Group: Su
  Data: <searchLink fieldCode="ZK" term="%22TECHNOLOGY+%26+ENGINEERING+%2F+Structural%22">TECHNOLOGY & ENGINEERING / Structural</searchLink><br /><searchLink fieldCode="ZK" term="%22TECHNOLOGY+%26+ENGINEERING+%2F+Sensors%22">TECHNOLOGY & ENGINEERING / Sensors</searchLink><br /><searchLink fieldCode="ZK" term="%22COMPUTERS+%2F+Data+Science+%2F+Machine+Learning%22">COMPUTERS / Data Science / Machine Learning</searchLink>
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=3678212
RecordInfo BibRecord:
  BibEntity:
    Classifications:
      – Code: 624.1710285
        Scheme: ddc
        Type: prePub
    Languages:
      – Code: eng
        Text: English
    Subjects:
      – SubjectFull: Structural analysis (Engineering)--Data processing
        Type: general
    Titles:
      – TitleFull: Data Driven Methods for Civil Structural Health Monitoring and Resilience : Latest Developments and Applications
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Mohammad Noori
      – PersonEntity:
          Name:
            NameFull: Carlo Rainieri
      – PersonEntity:
          Name:
            NameFull: Marco Domaneschi
      – PersonEntity:
          Name:
            NameFull: Vasilis Sarhosis
      – PersonEntity:
          Name:
            NameFull: Wael A. Altabey
      – PersonEntity:
          Name:
            NameFull: Mohammad Noori
      – PersonEntity:
          Name:
            NameFull: Carlo Rainieri
      – PersonEntity:
          Name:
            NameFull: Marco Domaneschi
      – PersonEntity:
          Name:
            NameFull: Vasilis Sarhosis
      – PersonEntity:
          Name:
            NameFull: Wael A. Altabey
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 01
              Type: published
              Y: 2023
            – D: 02
              M: 02
              Type: profile
              Y: 2024
          Identifiers:
            – Type: isbn-print
              Value: 9781032308371
            – Type: isbn-print
              Value: 9781032308388
            – Type: isbn-electronic
              Value: 9781000965551
            – Type: isbn-electronic
              Value: 9781000965582
            – Type: isbn-electronic
              Value: 9781003306924
          Titles:
            – TitleFull: Data Driven Methods for Civil Structural Health Monitoring and Resilience : Latest Developments and Applications
              Type: main
ResultId 1