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.
Highlights
An aging road and rail infrastructure has initiated and sustained a considerable body of research material over recent decades in the pursuit of enhanced certainty of structural condition and safety.Many bridge structures are subjected to traffic loading conditions far in advance of their original design criteria
Fan & Qiao [74] echoed many of these findings in their assessment of frequencies, mode shapes, Modal Curvature Method (MCM), and Damage Index Method (DIM), with superior damage sensitivity observed for higher modes, for modal curvature-based parameters, and a diminished performance overall when subjected to noise
The premise of the application of Hilbert-Huang Transformation (HHT) in this way stems from the fact that the majority of bridges are designed to behave linear-elastically under operational design loads, which implies that healthy bridges subjected to normal operational conditions should produce a nonlinear, but stationary dynamic response
Summary
An aging road and rail infrastructure has initiated and sustained a considerable body of research material over recent decades in the pursuit of enhanced certainty of structural condition and safety. Despite the magnitude of academic work on bridge damage detection and identification, the vast majority of in-service bridge data is still collected via visual inspections These methods are considered to be tried and trusted within the bridge-owner community, but possess several limitations in their scope and attain inconsistent results due to the disparity of inspector competency. Sci. 2017, 7, 510 work in a timely manner to minimise life cycle costs To this end, extensive research in the areas of bridge maintenance and maintenance scheduling [7,8,9], in addition to reliability analysis [10,11,12,13], has been conducted to assist the challenge, this is a work in progress. The structure of the paper is divided into two main sections, with the first section presenting the development of modal-based damage detection methodologies through the years and discussing their challenges and limitations. The second section is further sub-divided in order to deal with each identified issue individually and to discuss potential remedies that have been recently proposed in the literature
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