The papers in this Geotechnical Special Publication explain and demonstrate a broad range of methods of probabilistic site characterization using soil data obtained at one or more National Geotechnical Experimentation Sites (NGES), in particular the NGES locations at Texas A&M University, Treasure Island Naval Station, University of Massachusetts at Amherst, University of Houston, and Northwestern University. Among the topics covered are statistical estimation procedures based on homogenous random field theory, with as parameters the mean, coefficient of variation and scale of fluctuation, as well as two alternate fractal representations of spatial variation in soil deposits, a neural network model, and an approach to correlating soil properties based on the concept of fuzzy subsets. The aim of this publication is to provide both a state-of-the-art assessment of statistical methods of soil profile modeling and a set of baseline statistics, representative of the well-documented NGES, intended to serve as a priori information for probabilistic site characterization.

The papers in this Geotechnical Special Publication explain and demonstrate a broad range of methods of probabilistic site characterization using soil data obtained at one or more National Geotechnical Experimentation Sites (NGES), in particular the NGES locations at Texas A&M University, Treasure Island Naval Station, University of Massachusetts at Amherst, University of Houston, and Northwestern University. Among the topics covered are statistical estimation procedures based on homogenous random field theory, with as parameters the mean, coefficient of variation and scale of fluctuation, as well as two alternate fractal representations of spatial variation in soil deposits, a neural network model, and an approach to correlating soil properties based on the concept of fuzzy subsets. The aim of this publication is to provide both a state-of-the-art assessment of statistical methods of soil profile modeling and a set of baseline statistics, representative of the well-documented NGES, intended to serve as a priori information for probabilistic site characterization.