Volume 49, 2010, Number 2

In this study, the strength properties of polypropylene (PP)-fibre-reinforced fly-ash concrete were investigated experimentally and statistically. Three control factors (amount of fly ash, amount of PP fibre and curing time) were used for this study. The fly ash content that was used was 0, 60 and 120 kg/m(3) and the fibre content was 0, 0.45. 0.90 and 1.80 kg/m(3). The specimens were cured in standard curing conditions at temperature 23 +/- 2 degrees C for periods of 7, 28, 90 and 365 days. At the end of the curing period, the average of three specimens was tested to measure each of the concrete strength properties (compressive strength, flexural tensile strength and splitting tensile strength). Furthermore, the level of importance of these parameters on the strength properties was determined by rising the analysis of variance (Anova) method

TeV-Scale Black Hole Lifetimes in Extra-Dimensional Lovelock Gravity

We examine the mass loss rates and lifetimes of TeV-scale extra dimensional black holes (BH) in ADD-like models with Lovelock higher-curvature terms present in the action. In particular we focus on the predicted differences between the canonical and microcanonical ensemble statistical mechanics descriptions of the Hawking radiation that results in the decay of these BH. In even numbers of extra dimensions the employment of the microcanonical approach is shown to generally lead to a significant increase in the BH lifetime as in case of the Einstein-Hilbert action. For odd numbers of extra dimensions, stable BH remnants occur when employing either description provided the highest order allowed Lovelock invariant is present. However, in this case, the time dependence of the mass loss rates obtained employing the two approaches will be different. These effects are in principle measurable at future colliders.Comment: 27 pages, 9 figs; Refs. and discussion adde

Comparison of artificial neural network and fuzzy logic models for prediction of long-term compressive strength of silica fume concrete

In this study, an artificial neural network (ANN) and fuzzy logic (FL) study were developed to predict the compressive strength of silica fume concrete. A data set of a laboratory work, in which a total of 48 concretes were produced, was utilized in the ANNs and FL study. The concrete mixture parameters were four different water-cement ratios, three different cement dosages and three partial silica fume replacement ratios. Compressive strength of moist cured specimens was measured at five different ages. The obtained results with the experimental methods were compared with ANN and FL results. The results showed that ANN and FL can be alternative approaches for the predicting of compressive strength of silica fume concrete. © 2009 Elsevier Ltd. All rights reserved