Neural Network Modeling of Rheological Parameters of Grouts Containing Viscosity-Modifying Agent

The development of artificial neural network (ANN) models to predict the rheological behavior of grouts is described is this paper and the sensitivity of such parameters to the variation in mixture ingredients is also evaluated. The input parameters of the neural network were the mixture ingredients influencing the rheological behavior of grouts, namely the cement content, fly ash, ground-granulated blast-furnace slag, limestone powder, silica fume, water-binder ratio (w/b), high-range water-reducing admixture, and viscosity-modifying agent (welan gum). The six outputs of the ANN models were the mini-slump, the apparent viscosity at low shear, and the yield stress and plastic viscosity values of the Bingham and modified Bingham models, respectively. The model is based on a multi-layer feed-forward neural network. The details of the proposed ANN with its architecture, training, and validation are presented in this paper. A database of 186 mixtures from eight different studies was developed to train and test the ANN model. The effectiveness of the trained ANN model is evaluated by comparing its responses with the experimental data that were used in the training process. The results show that the ANN model can accurately predict the mini-slump, the apparent viscosity at low shear, the yield stress, and the plastic viscosity values of the Bingham and modified Bingham models of the pseudo-plastic grouts used in the training process. The results can also predict these properties of new mixtures within the practical range of the input variables used in the training with an absolute error of 2%, 0.5%, 8%, 4%, 2%, and 1.6%, respectively. The sensitivity of the ANN model showed that the trend data obtained by the models were in good agreement with the actual experimental results, demonstrating the effect of mixture ingredients on fluidity and the rheological parameters with both the Bingham and modified Bingham models.

238.2T-14 Concrete Thixotropy

Fresh concrete can exhibit different rheological behavior when at rest than when flowing. This difference is due to thixotropy, which can have important consequences for formwork pressure, multi-lift casting, slip-form paving, pumping, and segregation resistance. This TechNote defines thixotropy and distinguishes it from other changes in rheological properties; discusses the origins of, test methods for measuring, and factors affecting thixotropy; and concludes with its applications.

An examination of the rheological and mucoadhesive properties of poly(Acrylic acid) organogels designed as platforms for local drug delivery to the oral cavity

This study examined the rheological/mucoadhesive properties of poly (acrylic acid) PAA organogels as platforms for drug delivery to the oral cavity. Organogels were prepared using PAA (3%, 5%, 10% w/w) dissolved in ethylene glycol (EG), propylene glycol (PG), 1,3-propylene glycol (1,3-PG), 1,5-propanediol (1,5-PD), polyethylene glycol 400 (PEG 400), or glycerol. All organogels exhibited pseudoplastic flow. The increase in storage (G') and loss (G '') moduli of organogels as a function of frequency was minimal, G '' was greater than G '' (at all frequencies), and the loss tangent <1, indicative of gel behavior. Organogels prepared using EG, PG, and 1,3-propanediol (1,3-PD) exhibited similar flow/viscoelastic properties. Enhanced rheological structuring was associated with organogels prepared using glycerol (in particular) and PEG 400 due to their interaction with adjacent carboxylic acid groups on each chain and on adjacent chains. All organogels (with the exception of 1,5-PD) exhibited greater network structure than aqueous PAA gels. Organogel mucoadhesion increased with polymer concentration. Greatest mucoadhesion was associated with glycerol-based formulations, whereas aqueous PAA gels exhibited the lowest mucoadhesion. The enhanced network structure and the excellent mucoadhesive properties of these organogels, both of which may be engineered through choice of polymer concentration/solvent type, may be clinically useful for the delivery of drugs to the oral cavity.

Electrical conduction and rheological behaviour of composites of poly(epsilon-caprolactone) and MWCNTs

Two mechanisms of conduction were identified from temperature dependent (120 K-340 K) DC electrical resistivity measurements of composites of poly(c-caprolactone) (PCL) and multi-walled carbon nanotubes (MWCNTs). Activation of variable range hopping (VRH) occurred at lower temperatures than that for temperature fluctuation induced tunneling (TFIT). Experimental data was in good agreement with the VRH model in contrast to the TFIT model, where broadening of tunnel junctions and increasing electrical resistivity at T > T-g is a consequence of a large difference in the coefficients of thermal expansion of PCL and MWCNTs. A numerical model was developed to explain this behavior accounting for a thermal expansion effect by supposing the large increase in electrical resistivity corresponds to the larger relative deformation due to thermal expansion associated with disintegration of the conductive MWCNT network. MWCNTs had a significant nucleating effect on PCL resulting in increased PCL crystallinity and an electrically insulating layer between MWCNTs. The onset of rheological percolation at similar to 0.18 vol% MWCNTs was clearly evident as storage modulus, G' and complex viscosity, vertical bar eta*vertical bar increased by several orders of magnitude. From Cole-Cole and Van Gurp-Palmen plots, and extraction of crossover points (G(c)) from overlaying plots of G' and G '' as a function of frequency, the onset of rheological percolation at 0.18 vol% MWCNTs was confirmed, a similar MWCNT loading to that determined for electrical percolation.