A PROBABILISTIC ANALYSIS OF THE DISTRIBUTION OF COLLAPSING SOIL IN TUCSON USING KRIGING METHOD.
AuthorALI, MOLLA MOHAMMAD.
AdvisorMyers, Donald E.
Nowatzki, Edward A.
MetadataShow full item record
PublisherThe University of Arizona.
RightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
AbstractAn analytical investigation was carried to determine the nature and extent of the variability of selected collapse criteria and collapse-related soil parameters both areally and with depth within the city of Tucson. Collapse-related soil parameters of about 1000 sample points from over 400 borehole locations throughout the Tucson basin were collected from several consulting geotecnical engineering offices of the city. Statistical analysis on seven data sets corresponding to six different depth increments below the surface showed high dispersion tendencies expressed by the value of coeffecient of variation (cov). The value of cov was found to increase linearly with depth for most criteria and parameters. All the collapse criteria and collapse-related soil parameters were found to follow the Gamma distribution function except insitu dry density bd) and porosity (n₀) which were found to follow the Weibull distribution function. A polynomial regression model developed for the collapse parameter Cp showed that it varies with depth almost linearly. A stepwise regression analysis revealed that the collapse parameter Cp is strongly correlated with γd and insitu moisture content, woo Factor analysis validates this finding by producing two strong factors γd and insitu degree of saturation, s₀ which described almost 80% of the variation encountered in the data. The application of geostatistical concepts was found to be feasible in analyzing the collapse criteria and collapse-related soil parameters. Almost all criteria and parameters were strongly dependent spatially. A spherical variogram model was found to be appropriate for them. The method of Ordinary Kriging provided an unbiased estimation of a parameter at an unsampled location with known estimation variance. The method of Indicator Kriging was used to develop contour plots for the various data sets that showed the probability that the value of a certain parameter is above or below a critical level. These contour plots can be used to identify the areas within the City of Tucson that contain soils having a low- medium- or high-collapse potential. The ability to predict the occurence of such soils with a known degree of certitude is invaluable to planners, developers and geotechnical engineers.
Degree ProgramCivil Engineering and Engineering Mechanics