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Review

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

1
Departamento de Ingeniería Eléctrica y Electrónica, Universidad Nacional de Colombia, Cra 45 No. 26-85, Bogotá 111321, Colombia
2
Escuela de Tecnologías de la información y la comunicación, Politécnico Grancolombiano Institución Universitaria, Bogotá 111321, Colombia
3
Departamento de Ingeniería de Sistemas e Industrial, Universidad Nacional de Colombia, Cra 45 No. 26-85, Bogotá 111321, Colombia
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
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(3), 733; https://doi.org/10.3390/s20030733
Received: 8 November 2019 / Revised: 13 January 2020 / Accepted: 20 January 2020 / Published: 29 January 2020
(This article belongs to the Special Issue Sensors for Structural Health Monitoring and Condition Monitoring)
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. View Full-Text
Keywords: data-driven algorithms; damage identification; structural health monitoring; sensors data-driven algorithms; damage identification; structural health monitoring; sensors
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MDPI and ACS Style

Tibaduiza Burgos, D.A.; Gomez Vargas, R.C.; Pedraza, C.; Agis, D.; Pozo, F. Damage Identification in Structural Health Monitoring: A Brief Review from its Implementation to the Use of Data-Driven Applications. Sensors 2020, 20, 733. https://doi.org/10.3390/s20030733

AMA Style

Tibaduiza Burgos DA, Gomez Vargas RC, Pedraza C, Agis D, Pozo F. Damage Identification in Structural Health Monitoring: A Brief Review from its Implementation to the Use of Data-Driven Applications. Sensors. 2020; 20(3):733. https://doi.org/10.3390/s20030733

Chicago/Turabian Style

Tibaduiza Burgos, Diego A., Ricardo C. Gomez Vargas, Cesar Pedraza, David Agis, and Francesc Pozo. 2020. "Damage Identification in Structural Health Monitoring: A Brief Review from its Implementation to the Use of Data-Driven Applications" Sensors 20, no. 3: 733. https://doi.org/10.3390/s20030733

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