Filling Ability and Passing Ability of Self-Consolidating Concrete

This paper reviews statistical models obtained from a composite factorial design study, which was carried out to determine the influence of three key parameters of mixture composition on filling ability and passing ability of self-consolidating concrete (SCC). This study was a part of the European project “Testing SCC”- GRD2-2000-30024. The parameters considered in this study were the dosages of water and high-range water-reducing admixture (HRWRA), and the volume of coarse aggregates. The responses of the derived statistical models were slump flow, T50 , T60, V-funnel flow time, Orimet flow time, and blocking ratio (L-box). The retention of these tests was also measured at 30 and 60 minutes after adding the first water. The models are valid for mixtures made with 188 to 208 L/m3 (317 to 350 lb/yd3) of water, 3.8 to 5.8 kg/m3 (570 to 970 mL/100 kg of binder) of HRWRA, and 220 to 360 L/m3 (5.97 to 9.76 ft3/yd3) of coarse aggregates. The utility of such models to optimize concrete mixtures and to achieve a good balance between filling ability and passing ability is discussed. Examples highlighting the usefulness of the models are presented using isoresponse surfaces to demonstrate single and coupled effects of mixture parameters on slump flow, T50 , T60 , V-funnel flow time, Orimet flow time, and blocking ratio. The paper also illustrates the various trade-offs between the mixture parameters on the derived responses that affected the filling and the passing ability.

Factorial Design of Cement Slurries Containing Limestone Powder for Self-Consolidating Slurry-Infiltrated Fiber Concrete

Slurries with high penetrability for production of Self-consolidating Slurry Infiltrated Fiber Concrete (SIFCON) were investigated in this study. Factorial experimental design was adopted in this investigation to assess the combined effects of five independent variables on mini-slump test, plate cohesion meter, induced bleeding test, J-fiber penetration test and compressive strength at 7 and 28 days. The independent variables investigated were the proportions of limestone powder (LSP) and sand, the dosages of superplasticiser (SP) and viscosity agent (VA), and water-to-binder ratio (w/b). A two-level fractional factorial statistical method was used to model the influence of key parameters on properties affecting the behaviour of fresh cement slurry and compressive strength. The models are valid for mixes with 10 to 50% LSP as replacement of cement, 0.02 to 0.06% VA by mass of cement, 0.6 to 1.2% SP and 50 to 150% sand (% mass of binder) and 0.42 to 0.48 w/b. The influences of LSP, SP, VA, sand and W/B were characterised and analysed using polynomial regression which identifies the primary factors and their interactions on the measured properties. Mathematical polynomials were developed for mini-slump, plate cohesion meter, J-fiber penetration test, induced bleeding and compressive strength as functions of LSP, SP, VA, sand and w/b. The estimated results of mini-slump, induced bleeding test and compressive strength from the derived models are compared with results obtained from previously proposed models that were developed for cement paste. The proposed response models of the self-consolidating SIFCON offer useful information regarding the mix optimization to secure a highly penetration of slurry with low compressive strength

Thermogravimetric evaluation of perennial ryegrass (Lolium perenne) for the prediction of in vitro dry matter digestibility

Thermogravimetry (TG) can be used for assessing the compositional differences in grasses that relate to dry matter digestibility (DMD) determined by pepsin-cellulase assay. This investigation developed regression models for predicting DMD of herbage grass during one growing season using TG results. The calibration samples were obtained from a field trial of eight cultivars and two breeding lines. The harvested materials from five cuts were analysed by TG to identify differences in the combustion patterns within the range of 30-600 degrees C. The discrete results including weight loss, peak height, area, temperature, widths and residue of three decomposition peaks were regressed against the measured DMD values of the calibration samples. Similarly, continuous weight loss results of the same samples were also utilised to generate DMD models. The r(2) for validation of the discrete and the best continuous models were 0.90 and 0.95, respectively, and the two calibrations were validated using independent samples from 24 plots from a trial carried out in 2004. The standard error for prediction of the 24 samples by the discrete model (4.14%) was higher than that by the continuous model (2.98%). This study has shown that DMD of grass could be predicted from the TG results. The benefit of thermal analysis is the ability to detect and show changes in composition of cell wall fractions of grasses during different cuts in a year.