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Appl. Sci. 2017, 7(5), 510; doi:10.3390/app7050510

A State of the Art Review of Modal-Based Damage Detection in Bridges: Development, Challenges, and Solutions

Department of Civil and Environmental Engineering, Technical University of Catalonia-BarcelonaTech, 08034 Barcelona, Spain
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Authors to whom correspondence should be addressed.
Academic Editor: Gangbing Song
Received: 1 April 2017 / Revised: 4 May 2017 / Accepted: 9 May 2017 / Published: 13 May 2017
(This article belongs to the Special Issue Structural Health Monitoring (SHM) of Civil Structures)
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Abstract

Traditionally, damage identification techniques in bridges have focused on monitoring changes to modal-based Damage Sensitive Features (DSFs) due to their direct relationship with structural stiffness and their spatial information content. However, their progression to real-world applications has not been without its challenges and shortcomings, mainly stemming from: (1) environmental and operational variations; (2) inefficient utilization of machine learning algorithms for damage detection; and (3) a general over-reliance on modal-based DSFs alone. The present paper provides an in-depth review of the development of modal-based DSFs and a synopsis of the challenges they face. The paper then sets out to addresses the highlighted challenges in terms of published advancements and alternatives from recent literature. View Full-Text
Keywords: structural health monitoring; damage detection; modal analysis; machine learning; non-stationary analysis; signal processing structural health monitoring; damage detection; modal analysis; machine learning; non-stationary analysis; signal processing
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Moughty, J.J.; Casas, J.R. A State of the Art Review of Modal-Based Damage Detection in Bridges: Development, Challenges, and Solutions. Appl. Sci. 2017, 7, 510.

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