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The identification and stratification of soils represent an essential step in designing various geotechnical structures. The cone penetration test (CPT) measurements are used widely to classify the soil; however, the soil classification charts such as the Robertson chart undergo uncertainty from different sources that make overlapping of soil types. This article aims to develop a probabilistic approach employing clustering with Gaussian mixture model, which can deal with the uncertainty and classify the soil based on CPT. The spatial parameters were obtained assuming the different types of covariance matrices. The data utilized in this study represent the results of CPT in four locations in Nasiriyah, Iraq. Both spatial and feature patterns were produced and used to classify the soil. This research revealed that the soils deduced from the Robertson chart were clay, silt, and sand. No gravelly sand appeared on the chart. The soil at shallow depth was clay soils, and it changed to be sandy silt at fairly great depth. They were close to the boundary curve between the stiff clay and sand zones and relatively existed at great depth. The probabilistic approach can detect the soil layers fast without experience-based decisions. Moreover, the type of assumed covariance matrix may affect the soil profile.

Effect of the covariance matrix type on the CPT based soil stratification utilizing the Gaussian mixture model