A State of the Art Review of Modal-Based Damage Detection in Bridges: Development, Challenges, and Solutions
By J. J. Moughty, J. Rius. 2017
203 citations recorded by the source corpus.
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.
Subfields: Civil and Structural Engineering
View Paper: https://www.semanticscholar.org/paper/a9199d5289fc3d03de8240d8f6dcd347b3776f83
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In Civil and Structural Engineering
Citations in the corpus
- Review of Bridge Structural Health Monitoring Aided by Big Data and Artificial Intelligence: From Condition Assessment to Damage Detection (2020, unknown): “Frequency-domain features formed the basis of vibration-based damage detection methods and have acquired extensive applications in past decades (Doebling et al. 1998; Fan and Qiao 2011; Moughty and Casas 2017).”
- Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights (2021, background): “Moughty and Casas 30 overviewed the model-driven damage detection and alternative solutions.”