Abstract
The cone penetrometer test (CPT) has been widely used in geotechnical investigations. However, how to use the limited CPT data to reasonably predict the soil parameters of the unsampled regions remains a challenge. In the present study, we adopted the Kriging method to obtain the CPT data of an unsampled location in Adelaide, South Australia, based on the collected CPT data from six soundings around this location. Interpolation results showed that the trend of the estimated parameters is consistent with the trend of parameters of the surrounding points. From the Kriging interpolation result, we further carried out axial bearing capacity calculation of a precast concrete pile using the CPT-based direct method to verify the reliability of the method. The calculated bearing capacity of the pile is 99.6 kN which is very close to the true value of 102.8 kN. Our results demonstrated the effectiveness of the Kriging method in considering the soil spatial variability and predicting soil parameters, which is quite suitable for the application in engineering practice.
Highlights
Considering the spatial variability of the soil, the cone penetrometer test (CPT) data obtained from adjacent soundings could be quite different, which makes it difficult to estimate the soil properties at the unsampled locations
There are two ways to consider the correlation of the data sequence with distance: one is random field theory, and the other is geostatistics
Soulié [18] used semivariogram to analyze the spatial variability of CPT data performed in alluvial deposits of sand and gravel in the Mississippi River flood plain, and the results showed that the spherical model was more proper for fitting the actual curve compared with other models
Summary
How to use the limited CPT data to reasonably predict the soil parameters of the unsampled regions remains a challenge. From the Kriging interpolation result, we further carried out axial bearing capacity calculation of a precast concrete pile using the CPT-based direct method to verify the reliability of the method. Our results demonstrated the effectiveness of the Kriging method in considering the soil spatial variability and predicting soil parameters, which is quite suitable for the application in engineering practice. Considering the spatial variability of the soil, the CPT data obtained from adjacent soundings could be quite different, which makes it difficult to estimate the soil properties at the unsampled locations. For large areas, how to reasonably estimate the soil properties of the entire area through limited CPT data is of great importance.
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