Moses Karakouzian

Porosity dependence of the elastic modulus of lithophysae-rich tuff: Numerical and experimental investigations

Abstract The influence of porosity on the mechanical properties of rock has received much research attention. Portions of the Yucca Mountain high-level nuclear waste repository may be placed in tuff units containing lithophysal cavities, which are large, generally noninterconnected cavities that can be considered a form of macroscopic porosity. This paper presents the results of numerical modeling and uniaxial compression testing of analog models and tuff rock, in order to assess the relationships between elastic modulus and porosity. The first part of the paper presents numerical simulation of uniaxial compression testing to calculate the elastic modulus of two-dimensional models containing randomly distributed circular holes in plane strain. The range of porosities investigated is approximately 5–40%. In the second part, the elastic modulus determined from the uniaxial compression testing of analog models and tuff specimens is presented. The analog specimens were made of plaster of Paris containing varying amounts of spherical shaped Styrofoam® inclusions to simulate a cavity structure similar to tuff. The results from the numerical analysis and analog material testing show an exponential decrease in elastic modulus with increasing porosity, whereas the elastic moduli of tuff show a linear decrease. The difference in the two behaviors can be attributed to the nonuniform cavity shapes in the tuff specimens.

Investigation of the AP-42 Sampling Method

Abstract The AP-42 method has been recommended by the U.S. Environment Protection Agency to collect dust emission data. According to this method, the number of sampling sites needs to be determined first. At these sites, the dust will be collected based on plots drawn on the road surface. Apparently, there has been no systematic rule to follow to determine the number of sampling sites. In addition, it is not known whether the required number of plots and their sizes are validated based on real data. Mobile sampling technology can collect dust emission data at very close space intervals, which to some extent can be viewed as being close to actual dust emission data continuously distributed over roadway segments. With such data available, this study investigated the number of sampling sites and the number of plots and their sizes based on the optimal allocation sampling method and the Monte Carlo simulation method. The results from the optimal allocation method indicated that most of the sampling sites should be drawn from the local roads because the variance of emission and proportion of road segments of this roadway classification are significantly bigger than other roadway classifications. This observation may lead to the application of other cost-effective sampling approaches. The results from the Monte Carlo simulation method imply that clear patterns of improved estimation of emission factors versus plot number and size can be observed only for three roadway classifications, not for other classifications. This result indicates that the AP-42 method may not be applicable to some roadway classifications, and thus different data collection methods such as the mobile sampling technology may be necessary.

DISCRIMINANT ANALYSIS OF HYDROCOLLAPSE IN LAS VEGAS SOILS

Abstract In their natural condition, hydrocollapsible soils exhibit considerable strength. As they become wet, they exhibit considerable collapse, even in the absence of an applied load. The severity of this collapse depends on a variety of factors. One would like to be able to predict the severity of collapse from easily measurable properties of the soil - if not with absolute precision, at least in an approximate, probabilistic way. Using a hydrocollapse measurements database compiled from geotechnical reports on various clays, silts and sands found in the Las Vegas area, we apply linear discriminant analysis to determine the dependence of collapse percentage on four properties: moisture content, load, dry density, and depth. Using this analysis, we give a formula which estimates into which of three predetermined collapse ranges a given sample is most likely to fall, either based on all four properties or based on moisture content and dry density alone. We then derive a novel probabilistic classificatio...