The goal of Ground Penetrating Radar (GPR) research at the University of Arizona is to improve our understanding use of GPR in a variety of settings. Observations from local surveys will form the basis for developing continuous profiling systems for future terrestrial and extraterrestrial applications. Determining electrical properties of in-situ soils and rock helps predict GPR behavior. A test site was constructed to measure the electrical properties of representative Southern Arizona Basin soils and to quantify the GPR response to these properties. In general, 100 and 300 MHz antennas are useful for surveys designed to locate large anomalies. The higher resolution of the 500 MHz antenna is valuable in most investigations and has been successful in mapping archaeological sites in the region. Signal processing of digitized GPR data clarifies the profile for interpretation and conditions the data for remote interpretation of the GPR profile through neural network pattern recognition of anomalies.

<p> Flooding is recognized as a catastrophic event and a threat to the load carrying capacity of pavements around the world. In the aftermath of flooding, the pavement structure could be inundated and fully saturated. The significant increase of water within pavement layers may cause weakness and induce damage with traffic loading, subsequently increasing maintenance costs and shortening pavement service life. </p><p> The assessment of the structural performance and capacity of flooded pavements remains complicated due to lack of structural data immediately following flooding, and information about the pavement structure and materials is not always readily available. Currently, the decision to open roads for traffic is based on the assessment of the pavements, which relies on visual inspection and experience. An incorrect assessment of the flooded pavement structural capacity due to unforeseen conditions may lead to unexpected outcomes or failure. The objective of this dissertation is to advance the current knowledge of the behavior of flooded pavements, based on their performance properties and structural capacities. </p><p> Several methodologies have been developed and examined for a set of pavement structures with different material types using layered elastic analysis to (1) investigate the pavement response to traffic loads under different moisture conditions, (2) identify the important parameters that affect the performance of inundated pavements, (3) investigate the influence depth of the subsurface water level at which the road can withstand traffic with zero to minimum deterioration, (4) estimate the in-situ pavement surface deflection, and (5) identify the catastrophic failure of pavements in post-flood events. </p><p> The findings showed a significant reduction in structural capacity when the pavement structure was in the fully saturated condition, but the road could regain its capacity after desaturation and recession of water level. The influence depth for the subsurface water level was found to be dependent on pavement structure and material type. The most accurate method to estimate the in-situ measured deflection is to divide the soil layer into several layers in the layered elastic analysis. Accurate layer thicknesses, traffic type, and interlayer bond condition are the important factors for evaluating changes in expected horizontal strain at the bottom of asphalt layer, used for predicting fatigue cracking pavement performance. The type of base and subgrade materials are the most important factors for evaluating the changes in expected vertical strain at the top of subgrade layer, used to predict pavement rutting performance. This dissertation provides information to agencies that will enhance their understanding of the performance and structural capacity of pavements in post-flood events.</p>

Laboratory methods were used to define matrix hydraulic properties for low-permeability Apache Leap Tuff core segments. Moisture content/matric potential relationships, including hysteresis, and measured hydraulic conductivity data were determined at a constant laboratory temperature of 20°C. To investigate the effects of temperature on those relationships, additional retention data were obtained at 5°C and 45°C. Measured retention data at all temperatures were applied to the van Genuchten model RETC, which performs curve-fitting and calculation of the flow parameter hydraulic conductivity. Although data at 5°C proved to be inconclusive, increasing the temperature from 20 to 45°C produced a shift of the moisture characteristic curve toward a higher potential for a given water saturation. Model-calculated hydraulic conductivity also increased as temperature increased, with respect to water saturation. The temperature-dependent change in the viscosity of water proved inadequate to explain the increases of hydraulic conductivity with temperature.

Salt has unusual properties which complicate seismic exploration. A Model study examines some problems encountered in subsalt amplitude analysis and demonstrates that traditional techniques for detecting hydrocarbons and overpressured sediments using amplitude and velocity analysis are severely limited subsalt. Strong mode conversions generated at salt-sediment interfaces intrinsically limit amplitude versus offset techniques by diverting a significant amount of energy away from P-wave reflections. Some of the information lost from P-wave reflections is recouped by examining mode converted reflection amplitudes. Mode conversions are sensitive to different rock properties than P-waves, and those generated at the base of salt bodies are easily identified using acoustic and elastic modeling comparisons. Models demonstrate the use of base of salt converted mode amplitudes to gauge the strength of P-wave reflections for hydrocarbon discrimination. Further, mode converted amplitudes are used in conjunction with P-wave amplitudes from base of salt to detect overpressuring directly beneath salt.

The availability of high-resolution LIDAR and GIS data combined with the onset of more powerful computers has made the use of distributed parameter hydrologic models more feasible. This study employs one such model, Vflo(TM), along with a widely used hydraulic model, HEC-RAS, to simulate the runoff-response of a resaca network in Brownsville, TX using newly available LIDAR data. While previous studies have attempted to simulate the runoff-response of a resaca, they were greatly limited by the lack of detailed topographic data and information describing the hydraulic structures that connect one resaca pool to the next. Floodplain mapping in the area revealed that very little flooding occurs in and around the resacas compared to the two main drainage ditches in the area. Furthermore an evaluation of the available storage in the resacas shows great potential for the use of resacas as detention reservoirs in the future for excess stormwater.

Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2006. / Source: Dissertation Abstracts International, Volume: 67-07, Section: A, page: 2701. Adviser: Luc E. Anselin. Includes bibliographical references (leaves 160-181) Available on microfilm from Pro Quest Information and Learning.

No description available.

<p> Micropiles made their debut as a cost effective way to retrofit existing historical structures. Recently, micropiles have increased in popularity all over the world and are being used for bridges, buildings, slope stability, antenna towers, and residential construction. Micropiles excel in difficult drilling conditions where other deep foundation methods are not plausible and consist of any combination of grout, rebar, hollow bar, steel pin, and steel casing. Due to their slender nature, defined less than 300 mm in diameter and lengths up to 100 feet, micropiles offer a distinct challenge in quantifying load transfer behaviors. Research at the University at Buffalo investigated the load transfer behavior of a single micropile and the influence of steel casing in soil using the finite element software ABAQUS. Soil models of sand, clay, and rock were fabricated. Simulated load testing determined micropile axial and lateral capacities for various cased length ratios, the cased length to micropile length, and were compared to field load tests. For both the clay and sand models an increase in cased length ratio resulted in lower axial capacities and higher lateral capacities. For the lateral case, diminishing returns on lateral capacities are observed for cased length ratios over 1:2. An increase in axial capacity was observed when casing to shale rock. The results are compared to various case studies, typical construction practices, and current design methodologies.</p>

Eight 3D (three dimensional) rock joint geometry modeling schemes which investigate statistical homogeneity, and incorporate corrections for sampling biases and applications of stereology are presented. A procedure for verification of the developed models also is presented. In this study, shape of the joints was assumed as circular. The models provide the number of joint sets, and for each joint set, the intensity, orientation, spacing, location and diameter distributions. Miller's method (1983) with new interpretations (Kulatilake et al., 1990b) and equal area polar plots were used together to identify the largest statistically homogenous region around the ventilation drift, Stripa mine, data of which were used for both modeling and verification. Four joint sets were found in this region. A general vector approach to correct sampling bias on joint orientation is presented. Corrected data as well as raw data were subjected to chi-square goodness-of-fit tests to check the suitability of hemispherical normal and Bingham distributions in representing orientation of joint sets. Only raw data of joint set 4 followed Bingham distribution. Therefore, joint set orientations were best represented as empirical distributions. Two methods are presented for the modeling of joint spacing, linear intensity and location. In each method, spacing distributions of joint sets were best represented by exponential distributions. Then, joint intensity and location distributions are represented by Poisson and uniform distributions respectively. Correction of sampling bias on joint spacing also is presented. Joint size modeling was carried out using two methods: area sampling survey method and scanline sampling survey method. In these two methods, corrections of sampling biases associated with joint size modeling are presented. 3D joint sizes were inferred from 2D trace length measurements using geometrical probability and conditional probability concepts. In both methods diameter distributions are represented by gamma distributions. For verification, joints were generated in a volume according to the statistical models, using Monte-Carlo simulation. This volume was intersected by planes to obtain joint traces on exposures of size and shape similar to the ones used to obtain field data. Characteristics of these predicted joint traces were compared with the field data in a statistical sense. For the rock mass under this study, the modeling scheme 3 was found to be the most suitable scheme.

Many difficulties are encountered when analyzing large spatial data sets. The following research focuses on how various univariate, multivariate, geostatistical, and geographic information systems techniques can aid in the examination of spatial data sets containing large numbers of samples, as well as variables. The analyses are applied to data from Phase I of the Eastern Lake Survey which deals with the effects of acidic deposition on aquatic ecosystems. Relationships among chemical analytes are explored, and outlying or atypical lake samples and chemical analytes are identified using statistical techniques including univariate statistics, correlation analysis, normality testing, multivariate outlier testing, correspondence analysis, and ordinary kriging. The primary objective of the correspondence analysis was to obtain a reduction in the number of chemical analytes in order to make subsequent data analyses and interpretations easier. The main reason for performing a geostatistical analysis of the ELS-I data was to obtain maps of interpolated values for unsampled lake locations. Geographic information systems technology was used to examine the influence of lake size on the kriging analysis. This research should provide useful for future efforts to detect and ameliorate the effects of acidic deposition on aquatic ecosystems.