BRIDGE INTEGRITY ASSESSMENT BY CONTINUOUS WAVELET TRANSFORMS

2009 ◽  
Vol 09 (01) ◽  
pp. 11-43 ◽  
Author(s):  
A. ALVANDI ◽  
J. BASTIEN ◽  
E. GRÉGOIRE ◽  
M. JOLIN
Keyword(s):  
Wavelet Transforms ◽  
Damage Assessment ◽  
High Sensitivity ◽  
Assessment Procedure ◽  
Continuous Wavelet ◽  
Continuous Wavelet Transforms ◽  
Integrity Assessment ◽  
Key Characteristics ◽  
Existing Bridges ◽  
Assessment Procedures

The potential of continuous wavelet transforms for damage assessment of existing bridges is investigated herein. Different types of continuous wavelet transforms have been under investigation and the most effective ones have been introduced in a toolbox to automate the damage assessment procedure. In this paper, the performance of the wavelet approach and the influence of different parameters in the damage assessment procedures are studied through two examples: a simply supported beam and a three-span concrete bridge. Applying the wavelet transforms to a structure's static and/or dynamic response showed promising results with regard to localization of structural modification or damage. This paper underlines the high sensitivity of the wavelet analysis to damage intensity and its ability to be applied directly to the damaged data. These key characteristics could lead to this approach becoming one of the best for structural health monitoring of existing bridges in the near future.

Modeling of Wave Dispersion Using Continuous Wavelet Transforms

2005 ◽  
Vol 162 (5) ◽  
pp. 843-855 ◽  
Author(s):  
M. Kulesh ◽  
M. Holschneider ◽  
M. S. Diallo ◽  
Q. Xie ◽  
F. Scherbaum
Keyword(s):  
Wavelet Transforms ◽  
Wave Dispersion ◽  
Continuous Wavelet ◽  
Continuous Wavelet Transforms

A new potential field shape descriptor using continuous wavelet transforms

Geophysics ◽  
2020 ◽  
Vol 85 (5) ◽  
pp. G81-G92
Author(s):  
P. Cavalier ◽  
D. W. O’Hagan
Keyword(s):  
Potential Field ◽  
Wavelet Transforms ◽  
A Priori ◽  
Shape Descriptor ◽  
Continuous Wavelet ◽  
Source Depth ◽  
A Priori Information ◽  
Field Source ◽  
Continuous Wavelet Transforms ◽  
Priori Information

Potential field characterization aims at determining source depths, inclination, and type, preferably without a priori information. For ideal sources, the type is often defined from the field’s degree of homogeneity, derived from its expression in the space domain. We have developed a new shape descriptor for potential field source functions, stemming from spectral-domain parameters, which manifest clearly when using continuous wavelet transforms (CWTs). We generalize the use of the maximum wavelet coefficient points in the CWT diagram for the analysis of all types of potential fields (gravity, magnetic, and self-potential). We interpret the CWT diagram as a similarity diagram between the wavelet and the analyzed signal, which has fewer limitations than its interpretation as a weighted and upward-continued field projection. We develop new formulas for magnetic source depth prediction, as well as for effective inclination estimation, using various kinds of wavelets. We found that the potential field source functions exhibit precise behaviors in the CWT analysis that can be predicted using a single parameter [Formula: see text], which is related to their Fourier transforms. This parameter being scale and rotation-invariant can be used as a source-body shape descriptor similar to the commonly used structural index (SI). An advantage of the new descriptor is an increased level of discrimination between sources because it takes different values to describe the horizontal or the vertical cylinder structures. Our approach is illustrated on synthetic examples and real data. The method can be applied directly with the native form of the CWT without scaling factor modification, negative plane diagram extension, or downward plotting. This framework offers an alternative to existing wavelet-like projection methods or other classic deconvolution techniques relying on SI for determining the source depth, dip, and type without a priori information, with an increased level of differentiation between source structures thanks to the new shape descriptor.

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