This thesis reports the results of research on correlations between laboratory measured collapse and routinely measured soil properties. From graphs of each soil property versus percent collapse, it is determined that dry density and water content have the strongest relationship of the variables examined to collapse. Multiple linear regression was also utilized to explore the possibility of predicting percent collapse from depth, classification, dry density and water content. These two analyses indicate that predicting quantitative percent collapse may be too specific for this application. Bayes' Theorem is used to calculate the probability of collapse group membership for each zone. The resulting models give a score based on water content, dry density and soil classification that indicates the probability of collapse and noncollapse group membership. The models are a considerable improvement over chance but, additional validation is recommended. (Abstract shortened by UMI.).
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