Effective elastic parameters of random composites

1980 ◽  
Vol 37 (4) ◽  
pp. 377-379 ◽  
Author(s):  
A. J. Devaney ◽  
H. Levine
Keyword(s):  
Elastic Parameters ◽  
Random Composites

REFRACTION ARRIVALS THROUGH THIN HIGH‐VELOCITY LAYERS

Geophysics ◽  
1965 ◽  
Vol 30 (2) ◽  
pp. 204-212 ◽  
Author(s):  
J. H. Rosenbaum
Keyword(s):  
Exponential Decay ◽  
High Velocity ◽  
Elastic Layer ◽  
Approximate Formula ◽  
Asymptotic Theory ◽  
Surrounding Medium ◽  
Low Frequency ◽  
Horizontal Distance ◽  
Symmetric Mode ◽  
Elastic Parameters

The first significant refraction arrival through a thin high‐velocity elastic layer in an elastic medium has been investigated theoretically by means of an asymptotic theory. This first low‐frequency arrival is closely connected with the longitudinal plate wave in the thin layer. When the medium surrounding the layer is a fluid, the signal does not decay exponentially with horizontal distance; when the surrounding medium is a solid, the signal does decay exponentially. A very simple approximate formula for this exponential decay is presented and compared with numerical results of the more rigorous theory. The decay as well as the shape of the signal is dependent upon the contrast in elastic parameters between the plate and the surrounding medium. Higher‐frequency early arrivals, associated with the second symmetric mode, have also been investigated. They exhibit greater exponential decay with horizontal distance than the low‐frequency first arrivals.

scholarly journals Accurate Determination of Elastic Parameters for Multicomponent Membranes

2008 ◽  
Vol 100 (8) ◽  
Author(s):  
Stefan Semrau ◽  
Timon Idema ◽  
Laurent Holtzer ◽  
Thomas Schmidt ◽  
Cornelis Storm
Keyword(s):  
Accurate Determination ◽  
Elastic Parameters

DIPLOBACK: Neural-Network-Based Backcalculation Program for Composite Pavements

1997 ◽  
Vol 1570 (1) ◽  
pp. 143-150 ◽  
Author(s):  
Lev Khazanovich ◽  
Jeffery Roesler
Keyword(s):  
Neural Network ◽  
Winkler Foundation ◽  
Multilayer Composite ◽  
Falling Weight Deflectometer ◽  
Subgrade Reaction ◽  
Elastic Parameters ◽  
The Neural Networks ◽  
Elastic Layers ◽  
Falling Weight ◽  
Moduli Of Elasticity

A neural-network-based backcalculation procedure is developed for multilayer composite pavement systems. The constructed layers are modeled as compressible elastic layers, whereas the subgrade is modeled as a Winkler foundation. The neural networks are trained to find moduli of elasticity of the constructed layers and a coefficient of subgrade reaction to accurately match a measured deflection profile. The method was verified by theoretically generated deflection profiles and falling weight deflectometer data measurements conducted at Edmonton Municipal Airport, Canada. For the theoretical deflection basins, the results of backcalculation were compared with actual elastic parameters, and excellent agreement was observed. The results of backcalculation using field test data were compared with the results obtained using WESDEF. Similar trends were observed for elastic parameters of all the pavement layers. The backcalculation procedure is implemented in a computer program called DIPLOBACK.

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