Gaussian curvature as an indicator used for damage detection of bridge structures

2021 ◽  
pp. 192-199
Author(s):  
T. Wu ◽  
L. Tang ◽  
C.Y. Jian ◽  
R.Y. Mao ◽  
Z.X. Zhou
Keyword(s):  
Damage Detection ◽  
Gaussian Curvature ◽  
Bridge Structures

scholarly journals Information-theoretical noninvasive damage detection in bridge structures

2016 ◽  
Vol 26 (11) ◽  
pp. 116312 ◽  
Author(s):  
Amila Sudu Ambegedara ◽  
Jie Sun ◽  
Kerop Janoyan ◽  
Erik Bollt
Keyword(s):  
Damage Detection ◽  
Bridge Structures

PolyVinyliDene Fluoride (PVDF) Material Based Energy Harvesting from Train and Damaged Bridge Interaction

2013 ◽  
Vol 569-570 ◽  
pp. 335-341 ◽  
Author(s):  
Paul Cahill ◽  
Nathan Jackson ◽  
Alan Mathewson ◽  
Vikram Pakrashi
Keyword(s):  
Finite Element ◽  
Energy Harvesting ◽  
Damage Detection ◽  
Polyvinylidene Fluoride ◽  
Element Model ◽  
Element Analysis ◽  
Operational Conditions ◽  
Existing Structures ◽  
Bridge Structures ◽  
New Generation

This paper investigates the potential use of PolyVinyliDene Fluoride (PVDF) for the purposes of damage detection for infrastructural elements, primarily for bridge elements. PVDF based sensors have been created and characterised in the laboratory in this regard. Finite element analysis of vehicle-bridge interactions with varying damage are carried out. The energy harvesting signatures of realistic trains are assessed and quantified for the modelled bridge. The effect of localized damage on the finite element model and its subsequent relationship with energy harvesting from the calibrated PVDF based sensors are investigated using the harvesting signatures of realistic trains. This approach is useful in terms of designing new generation smart bridge structures and for possible retrofit of existing structures. The use of train-bridge interaction ensures that the damage detection is carried out while the bridge is under operational conditions. Consequently, there is minimal to no impact on the existing operation of the bridge or the transport network during damage detection. The paper is expected to be useful for practicing engineers and researchers in the field of application of new materials in the next generation of bridge structures.

Structural Health Monitoring

2011 ◽  
pp. 230-243
Author(s):  
Liang Wang ◽  
Tommy Chan ◽  
David Thambiratnam ◽  
Andy Tan
Keyword(s):  
Signal Processing ◽  
Structural Health Monitoring ◽  
Damage Detection ◽  
Statistical Methods ◽  
Health Monitoring ◽  
Transportation Network ◽  
Structural Health ◽  
Before And After ◽  
Bridge Structures ◽  
Wavelet Techniques

<P>Structural health is a vital aspect of infrastructure sustainability. As a part of a vital infrastructure and transportation network, bridge structures must function safely at all times. However, due to heavier and faster moving vehicular loads and function adjustment, such as Busway accommodation, many bridges are now operating at an overload beyond their design capacity. Additionally, the huge renovation and replacement costs are a difficult burden for infrastructure owners. The structural health monitoring (SHM) systems proposed recently are incorporated with vibration-based damage detection techniques, statistical methods and signal processing techniques and have been regarded as efficient and economical ways to assess bridge condition and foresee probable costly failures. In this chapter, the recent developments in damage detection and condition assessment techniques based on vibration-based damage detection and statistical methods are reviewed. The vibration-based damage detection methods based on changes in natural frequencies, curvature or strain modes, modal strain energy, dynamic flexibility, artificial neural networks, before and after damage, and other signal processing methods such as Wavelet techniques, empirical mode decomposition and Hilbert spectrum methods are discussed in this chapter.&nbsp;&nbsp; </P>

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