No abstract

No abstract

Mechanical properties of materials can be derived from the force-displacement relationship through instrumented indentation tests. Complications arise when establishing the full elastic-plastic stress-strain relationship as the accuracy depends on how the material’s and indenter’s parameters are incorporated. For instance, the effect of the material work-hardening phenomenon such as the pile-up and sink-in effect cannot be accounted for with simplified analytical indentation solutions. Due to this limitation, this paper proposes a new inverse analysis approach based on dimensional functions analysis and artificial neural networks (ANNs). A database of the dimensional functions relating stress and strain parameters of materials has been developed. The database covers a wide range of engineering materials that have the yield strength-to-modulus ratio (σy/E) between 0.001 to 0.5, the work-hardening power (n) between 0–0.5, Poisson’s ratio (v) between 0.15–0.45, and the indentation angle (θ) between 65–80 degrees. The proposed algorithm enables determining the nanomechanical stress-strain parameters using the indentation force-displacement relationship, and is applicable to any materials that the properties are within the database range. The obtained results are validated with the conventional test results of steel and aluminum samples. To further demonstrate the application of the proposed algorithm, the nanomechanical stress-strain parameters of ordinary Portland cement phases were determined.

No abstract

This paper presents creep properties of cement and alkali activated fly ash (AAFA) paste and mortar determined from statistical analysis of nanoindentation data. Cement paste having 95 MPa compressive strength at 28 days was tested for comparison and validation with a conventional test. Using nanoindentation, the specific creep of the cement paste after one year was predicted as 18.32 microstrain/MPa. For AAFA samples, an experimental program was set up using Taguchi's Design of Experiment method to consider four parameters, silica fume, sand to binder ratio, liquid to solid ratio, and superplasticiser, each with three variations.Using ANOVA, the percentage contributions of these parameters on the creep modulus of AAFA samples are: silica fume 26%, sand to binder ratio 21%, liquid to solid ratio 22%, and superplasticiser 31%. The results using deconvolution technique to identify the creep modulus of different phases of AAFA matrices show that partly-activated, non-activated slag and non-activated compact glass phases are leading the creep behaviour of AAFA samples due to their high creep modulus. Compare to other parameters, the liquid to solid ratio contributes the most to the creep property of partly-activated slag, non-activated slag and non-activated compact glass phases, that is, 51%, materials are such as blended cement, alkali-activated pozzolan cement, and Geopolymer. This paper presents the creep properties of some of these materials and how different compositions in the mixtures are affecting their creep properties. The main aim is to obtain the optimum mix design, within the range of the parameters considered, for the minimum creep behaviour.15The measurement of viscoelastic properties of a material can be obtained by considering the nature and the rate of configurational rearrangements and the interaction of the properties. Creep testing involves applying a constant instan-