Damage Identification in Structural Health Monitoring: A Brief Review from its Implementation to the Use of Data-Driven Applications

The damage identification process provides relevant information about the current state of a structure under inspection, and it can be approached from two different points of view. The first approach uses data-driven algorithms, which are usually associated with the collection of data using sensors....

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Published in:Sensors (Basel, Switzerland) Vol. 20; no. 3; p. 733
Main Authors: Tibaduiza Burgos, Diego A., Gomez Vargas, Ricardo C., Pedraza, Cesar, Agis, David, Pozo, Francesc
Format: Journal Article Publication
Language:English
Published: Switzerland MDPI AG 29.01.2020
Multidisciplinary Digital Publishing Institute (MDPI)
MDPI
Subjects:
Algorithms
Aprenentatge automàtic
Civil engineering
Damage identification
Data-driven algorithms
Detectors
Enginyeria elèctrica
Estadística matemàtica
Internet of Things
Machine learning
Matemàtiques i estadística
Mathematical statistics
Microelectromechanical systems
Review
Sensors
Structural health monitoring
Useful life
Àrees temàtiques de la UPC
structural health monitoring
sensors
data-driven algorithms
damage identification
ISSN:1424-8220, 1424-8220
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Abstract The damage identification process provides relevant information about the current state of a structure under inspection, and it can be approached from two different points of view. The first approach uses data-driven algorithms, which are usually associated with the collection of data using sensors. Data are subsequently processed and analyzed. The second approach uses models to analyze information about the structure. In the latter case, the overall performance of the approach is associated with the accuracy of the model and the information that is used to define it. Although both approaches are widely used, data-driven algorithms are preferred in most cases because they afford the ability to analyze data acquired from sensors and to provide a real-time solution for decision making; however, these approaches involve high-performance processors due to the high computational cost. As a contribution to the researchers working with data-driven algorithms and applications, this work presents a brief review of data-driven algorithms for damage identification in structural health-monitoring applications. This review covers damage detection, localization, classification, extension, and prognosis, as well as the development of smart structures. The literature is systematically reviewed according to the natural steps of a structural health-monitoring system. This review also includes information on the types of sensors used as well as on the development of data-driven algorithms for damage identification.
AbstractList The damage identification process provides relevant information about the current state of a structure under inspection, and it can be approached from two different points of view. The first approach uses data-driven algorithms, which are usually associated with the collection of data using sensors. Data are subsequently processed and analyzed. The second approach uses models to analyze information about the structure. In the latter case, the overall performance of the approach is associated with the accuracy of the model and the information that is used to define it. Although both approaches are widely used, data-driven algorithms are preferred in most cases because they afford the ability to analyze data acquired from sensors and to provide a real-time solution for decision making; however, these approaches involve high-performance processors due to the high computational cost. As a contribution to the researchers working with data-driven algorithms and applications, this work presents a brief review of data-driven algorithms for damage identification in structural health-monitoring applications. This review covers damage detection, localization, classification, extension, and prognosis, as well as the development of smart structures. The literature is systematically reviewed according to the natural steps of a structural health-monitoring system. This review also includes information on the types of sensors used as well as on the development of data-driven algorithms for damage identification.
The damage identification process provides relevant information about the current state of a structure under inspection, and it can be approached from two different points of view. The first approach uses data-driven algorithms, which are usually associated with the collection of data using sensors. Data are subsequently processed and analyzed. The second approach uses models to analyze information about the structure. In the latter case, the overall performance of the approach is associated with the accuracy of the model and the information that is used to define it. Although both approaches are widely used, data-driven algorithms are preferred in most cases because they afford the ability to analyze data acquired from sensors and to provide a real-time solution for decision making; however, these approaches involve high-performance processors due to the high computational cost. As a contribution to the researchers working with data-driven algorithms and applications, this work presents a brief review of data-driven algorithms for damage identification in structural health-monitoring applications. This review covers damage detection, localization, classification, extension, and prognosis, as well as the development of smart structures. The literature is systematically reviewed according to the natural steps of a structural health-monitoring system. This review also includes information on the types of sensors used as well as on the development of data-driven algorithms for damage identification. Peer Reviewed
The damage identification process provides relevant information about the current state of a structure under inspection, and it can be approached from two different points of view. The first approach uses data-driven algorithms, which are usually associated with the collection of data using sensors. Data are subsequently processed and analyzed. The second approach uses models to analyze information about the structure. In the latter case, the overall performance of the approach is associated with the accuracy of the model and the information that is used to define it. Although both approaches are widely used, data-driven algorithms are preferred in most cases because they afford the ability to analyze data acquired from sensors and to provide a real-time solution for decision making; however, these approaches involve high-performance processors due to the high computational cost. As a contribution to the researchers working with data-driven algorithms and applications, this work presents a brief review of data-driven algorithms for damage identification in structural health-monitoring applications. This review covers damage detection, localization, classification, extension, and prognosis, as well as the development of smart structures. The literature is systematically reviewed according to the natural steps of a structural health-monitoring system. This review also includes information on the types of sensors used as well as on the development of data-driven algorithms for damage identification.The damage identification process provides relevant information about the current state of a structure under inspection, and it can be approached from two different points of view. The first approach uses data-driven algorithms, which are usually associated with the collection of data using sensors. Data are subsequently processed and analyzed. The second approach uses models to analyze information about the structure. In the latter case, the overall performance of the approach is associated with the accuracy of the model and the information that is used to define it. Although both approaches are widely used, data-driven algorithms are preferred in most cases because they afford the ability to analyze data acquired from sensors and to provide a real-time solution for decision making; however, these approaches involve high-performance processors due to the high computational cost. As a contribution to the researchers working with data-driven algorithms and applications, this work presents a brief review of data-driven algorithms for damage identification in structural health-monitoring applications. This review covers damage detection, localization, classification, extension, and prognosis, as well as the development of smart structures. The literature is systematically reviewed according to the natural steps of a structural health-monitoring system. This review also includes information on the types of sensors used as well as on the development of data-driven algorithms for damage identification.
Author Tibaduiza Burgos, Diego A.
Pozo, Francesc
Gomez Vargas, Ricardo C.
Pedraza, Cesar
Agis, David
AuthorAffiliation 3 Departamento de Ingeniería de Sistemas e Industrial, Universidad Nacional de Colombia, Cra 45 No. 26-85, Bogotá 111321, Colombia; capedrazab@unal.edu.co
4 Control, Modeling, Identification and Applications (CoDAlab), Departament de Matemàtiques, Escola d’Enginyeria de Barcelona Est (EEBE), Universitat Politècnica de Catalunya (UPC), Campus Diagonal-Besòs (CDB), Eduard Maristany, 16, 08019 Barcelona, Spain; david.agis@upc.edu (D.A.); francesc.pozo@upc.edu (F.P.)
1 Departamento de Ingeniería Eléctrica y Electrónica, Universidad Nacional de Colombia, Cra 45 No. 26-85, Bogotá 111321, Colombia; rcgomezv@unal.edu.co
2 Escuela de Tecnologías de la información y la comunicación, Politécnico Grancolombiano Institución Universitaria, Bogotá 111321, Colombia
AuthorAffiliation_xml – name: 2 Escuela de Tecnologías de la información y la comunicación, Politécnico Grancolombiano Institución Universitaria, Bogotá 111321, Colombia
– name: 4 Control, Modeling, Identification and Applications (CoDAlab), Departament de Matemàtiques, Escola d’Enginyeria de Barcelona Est (EEBE), Universitat Politècnica de Catalunya (UPC), Campus Diagonal-Besòs (CDB), Eduard Maristany, 16, 08019 Barcelona, Spain; david.agis@upc.edu (D.A.); francesc.pozo@upc.edu (F.P.)
– name: 1 Departamento de Ingeniería Eléctrica y Electrónica, Universidad Nacional de Colombia, Cra 45 No. 26-85, Bogotá 111321, Colombia; rcgomezv@unal.edu.co
– name: 3 Departamento de Ingeniería de Sistemas e Industrial, Universidad Nacional de Colombia, Cra 45 No. 26-85, Bogotá 111321, Colombia; capedrazab@unal.edu.co
Author_xml – sequence: 1
  givenname: Diego A.
  orcidid: 0000-0002-4498-596X
  surname: Tibaduiza Burgos
  fullname: Tibaduiza Burgos, Diego A.
– sequence: 2
  givenname: Ricardo C.
  orcidid: 0000-0003-2296-8648
  surname: Gomez Vargas
  fullname: Gomez Vargas, Ricardo C.
– sequence: 3
  givenname: Cesar
  orcidid: 0000-0002-6687-1429
  surname: Pedraza
  fullname: Pedraza, Cesar
– sequence: 4
  givenname: David
  orcidid: 0000-0002-7283-6902
  surname: Agis
  fullname: Agis, David
– sequence: 5
  givenname: Francesc
  orcidid: 0000-0001-8958-6789
  surname: Pozo
  fullname: Pozo, Francesc
BackLink https://www.ncbi.nlm.nih.gov/pubmed/32013073$$D View this record in MEDLINE/PubMed
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ContentType Journal Article
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Contributor Universitat Politècnica de Catalunya. CoDAlab - Control, Modelització, Identificació i Aplicacions
Universitat Politècnica de Catalunya. Doctorat en Matemàtica Aplicada
Universitat Politècnica de Catalunya. Departament de Matemàtiques
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– sequence: 3
  fullname: Universitat Politècnica de Catalunya. CoDAlab - Control, Modelització, Identificació i Aplicacions
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Issue 3
Keywords structural health monitoring
sensors
data-driven algorithms
damage identification
Language English
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SubjectTerms Algorithms
Aprenentatge automàtic
Civil engineering
Damage identification
Data-driven algorithms
Detectors
Enginyeria elèctrica
Estadística matemàtica
Internet of Things
Machine learning
Matemàtiques i estadística
Mathematical statistics
Microelectromechanical systems
Review
Sensors
Structural health monitoring
Useful life
Àrees temàtiques de la UPC
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Title Damage Identification in Structural Health Monitoring: A Brief Review from its Implementation to the Use of Data-Driven Applications
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