Compressive strength and stability of sustainable self-consolidating concrete containing fly ash,silica fume,and GGBS

This paper presents the findings of an experimental program seeking to understand the effect of mineral admixtures on fresh and hardened properties of sustainable self-consolidating concrete (SCC) mixes where up to 80% of Portland cement was replaced with fly ash, silica fume, or ground granulated blast furnace slag. Compressive strength of SCC mixes was measured after 3, 7, and 28 days of moist curing. It was concluded in this study that increasing the dosage of fly ash increases concrete flow but also decreases segregation resistance. In addition, for the water-to-cement ratio of 0.36 used in this study, it was observed that the compressive strength decreases compared to control mix after 28 days of curing when cement was partially replaced by 10%, 30%, and 40%of fly ash. However, a fly ash replacement ratio of 20% increased the compressive strength by a small margin compared to the control mix. Replacing cement with silica fume at 5%, 10%, 15%, and 20% was found to increase compressive strength of SCC mixes compared to the control mix. However, the highest 28 day compressive strength of 95.3 MPa occurred with SCC mixes in which 15% of the cement was replaced with silica fume.     

Effects of Vegetation Cover on the Radar Sensitivity to Soil Moisture

Measurements of the backscattering coefficient ?°, made for bare and vegetation-covered fields, are used in conjunction with a simple backscattering model to evaluate the effects of vegetation cover on the estimation accuracy of soil moisture when derived from radar observations. The results indicate that for soil moisture values below 50 percent of field capacity, the backscatter contribution of the vegetation cover limits the radar's ability to predict soil moisture with an acceptable degree of accuracy. However, for moisture values in the range between 50 and 150 percent of field capacity, the measured ?° is dominated by the soil contribution and the effects of vegetation cover become secondary in importance. It is estimated that in this upper soil moisture range, which is the primary range of interest in hydrology and agriculture, a radar soil moisture prediction algorithm would predict soil moisture with an error of less than ±15 percent of field capacity in 90 percent of the cases.