Arun Prasad

Strength and Durability of Lime-Treated Jarosite Waste Exposed to Freeze and Thaw

AbstractAn alternate freezing and thawing cycle (F-T) is a weathering process that is very common in cold regions. In a laboratory, the durability of any stabilized or nonstabilized material can be...

Parameters controlling strength of red mud-lime mix

Abstract The present work enumerates the effect of lime, water content, dry density, water/lime ratio and porosity/lime ratio on the unconfined compressive strength (q u ) of the red mud-lime mix at 28 days curing. The lime content has been varied from 3 to 11%. The results showed that the unconfined compressive strength (q u ) increases non-linearly with the increase in lime content for the red mud-lime mix. It was also found that the strength increases with the decrease in both, porosity (η) and inverse of volumetric lime content (L v ); whereas, it decreases with the decrease in dry density for all the red mud-lime mix studied. A power model fits well with a fair degree of correlation between unconfined compressive strength and porosity, and between the unconfined compressive strength and inverse of the volumetric lime content. Finally, it was concluded that the porosity/adjusted volumetric lime content ratio is the key parameter and it plays an essential role in the assessment of strength for the range of red mud-lime mix studied.

Strength retrieval of artificially cemented bauxite residue using machine learning: an alternative design approach based on response surface methodology

The aim of the present study is to propose an alternative artificial neural network model based on response surface methodology over conventional approach to estimate the unconfined compressive strength of artificially cemented bauxite residue. The artificial neural network model uses molding moisture content (w), curing time (t) and porosity/volumetric lime (η/Lv′) as input parameters and unconfined compressive strength as the output parameter. Bayesian regularization as training function with sigmoid and pure linear at hidden and output layers is used for modeling the artificial neural network. The proposed response surface methodology designed ANN model is comparable with the conventional designed ANN model and can be used effectively with significantly less number of data set. Sensitivity analysis, to make out the significant input factors based on connection-weight approach, is also discussed. Further, neural interpretation diagram is incorporated to study the effects of individual input parameters over the response. Finally, a predictive equation is presented based on response surface methodology designed artificial neural network model for the range of parameters studied.

Variables controlling strength of lime stabilized jarosite waste

The study advocates the influence of lime (L) and curing period (t) on stabilization of jarosite waste. A number of laboratory strength tests [unconfined compressive (qu) and split tensile strength (qt)] are conducted on artificially cemented jarosite-lime blends (limeu2009=u20092.5–10%) with different curing periods (tu2009=u20097, 28 and 90xa0days). The outcomes indicate that both qu and qt increase nonlinearly with the increase in lime content and curing period, which is further justified by microstructural study that illustrates the occurrence of larger agglomeration in particles. In addition to these, a good correlation between qu and qt and L and t was possible by fitting the power function on the outcomes. Furthermore, a unique relationship between qu and qt is also developed, which is independent of L and t (i.e. qt/quu2009=u20090.16).

1D Infiltration Behavior of Two-Layered Recycled Concrete Aggregates Using Hydrophobic Materials in a Column Apparatus

AbstractThe infiltration characteristics of two different hydrophobic materials were investigated in this study. The materials used in this study were fine and coarse recycled concrete aggregates (...