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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Bibliographic Details
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
Online Access:Get full text
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Summary: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.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s20030733