3 resultados para network measurements

em University of Queensland eSpace - Australia


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The extent of swelling of cross-linked poly(dimethylsiloxane) and linear low-density poly(ethylene) in supercritical CO2 has been investigated using high-pressure NMR spectroscopy and microscopy. Poly(dimethylsiloxane) was cross-linked to four different cross-link densities and swollen in supercritical CO2. The Flory-Huggins interaction parameter, x, was found to be 0.62 at 300 bar and 45 degrees C, indicating that supercritical CO2 is a relatively poor solvent compared to toluene or benzene. Linear low-density poly(ethylene) was shown to exhibit negligible swelling upon exposure to supercritical CO2 up to 300 bar. The effect Of CO2 pressure on the amorphous region of the poly(ethylene) was investigated by observing changes in the H-1 T-2 relaxation times of the polymer. These relaxation times decreased with increasing pressure, which was attributed to a decrease in mobility of the polymer chains as a result of compressive pressure.

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This paper reports on the development of an artificial neural network (ANN) method to detect laminar defects following the pattern matching approach utilizing dynamic measurement. Although structural health monitoring (SHM) using ANN has attracted much attention in the last decade, the problem of how to select the optimal class of ANN models has not been investigated in great depth. It turns out that the lack of a rigorous ANN design methodology is one of the main reasons for the delay in the successful application of the promising technique in SHM. In this paper, a Bayesian method is applied in the selection of the optimal class of ANN models for a given set of input/target training data. The ANN design method is demonstrated for the case of the detection and characterisation of laminar defects in carbon fibre-reinforced beams using flexural vibration data for beams with and without non-symmetric delamination damage.