Analytical and numerical techniques to predict carbon nanotubes properties

In this paper, two different approaches for modeling the behaviour of carbon nanotubes are presented. The first method models carbon nanotubes as an inhomogeneous cylindrical network shell using the asymptotic homogenization method. Explicit formulae are derived representing Young's and shear moduli of single-walled nanotubes in terms of pertinent material and geometric parameters. As an example, assuming certain values for these parameters, the Young's modulus was found to be 1.71 TPa, while the shear modulus was 0.32 TPa. The second method is based on finite element models. The inter-atomic interactions due to covalent and non-covalent bonds are replaced by beam and spring elements, respectively, in the structural model. Correlations between classical molecular mechanics and structural mechanics are used to effectively model the physics governing the nanotubes. Finite element models are developed for single-, double- and multi-walled carbon nanotubes. The deformations from the finite element simulations are subsequently used to predict the elastic and shear moduli of the nanotubes. The variation of mechanical properties with tube diameter is investigated for both zig-zag and armchair configurations. Furthermore, the dependence of mechanical properties on the number of nanotubules in multi-walled structures is also examined. Based on the finite element model, the value for the elastic modulus varied from 0.9 to 1.05 TPa for single and 1.32 to 1.58 TPa for double/multi-walled nanotubes. The shear modulus was found to vary from 0.14 to 0.47 TPa for single-walled nanotubes and 0.37 to 0.62 for double/multi-walled nanotubes

Embedded smart GFRP reinforcements for monitoring reinforced concrete flexural components

The main objectives of this paper are to demonstrate the feasibility of using newly developed smart GFRP reinforcements to effectively monitor reinforced concrete beams subjected to flexural and creep loads, and to develop non-linear numerical models to predict the behavior of these beams. The smart glass fiber-reinforced polymer (GFRP) rebars are fabricated using a modified pultrusion process, which allows the simultaneous embeddement of Fabry-Perot fiber-optic sensors within them. Two beams are subjected to static and repeated loads (until failure), and a third one is under long-term investigation for assessment of its creep behavior. The accuracy and reliability of the strain readings from the embedded sensors are verified by comparison with corresponding readings from surface attached electrical strain gages. Nonlinear finite element modeling of the smart concrete beams is subsequently performed. These models are shown to be effective in predicting various parameters of interest such as crack patterns, failure loads, strains and stresses. The strain values computed by these numerical models agree well with corresponding readings from the embedded fiber-optic sensors