Development of analytical and numerical models for predicting the mechanical properties of structural adhesives under curing using the PZT-based wave propagation technique
Journal Publication ResearchOnline@JCUAbstract
Structural adhesives are commonly used as bonding agents in fibre-reinforced polymer based strengthening systems for concrete structures. The effectiveness and integrity of the bonding layer can be ascertained by monitoring the stiffness and strength development of the structural adhesive throughout the curing process. Previous studies have shown that the piezoelectric-based wave propagation (WP) technique is a viable technology for such monitoring. While there is a limited number of experimental data that verifies the technology, there are no known modelling studies. This paper therefore reports the first analytical and numerical modelling study for the WP technique in monitoring the curing process of structural adhesives. An experimental program is also reported to support the modelling. Tests are firstly conducted with the WP technique to obtain the pressure wave (P-wave) velocity. Tensile tests are then conducted to determine the ultimate tensile strength and static modulus of elasticity of the structural adhesives. Based on the P-wave velocity, the dynamic modulus of elasticity can be evaluated analytically from the elastic wave equation as well as numerically through model updating with the development of a three-dimensional coupled field finite element model. Finally, semi-analytical and semi-numerical relationships are established to predict the ultimate tensile strength of the structural adhesives from the P-wave velocity. This proof-of-concept study shows that the WP technique is capable of continuous and real-time monitoring of the curing process of structural adhesives. With the aid of the models, the WP technique can potentially eliminate the need to conduct destructive tensile tests.
Journal
Mechanical Systems and Signal Processing
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Volume
128
ISBN/ISSN
1096-1216
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Pages Count
19
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Publisher
Elsevier
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DOI
10.1016/j.ymssp.2019.03.030