Abstract
The cone penetration test (CPT) has been widely used in many geotechnical engineering applications, including soil identification and classification, and evaluation of different soil properties. However, the uncertainties associated with site variation are typical characteristics of subsurface soil conditions that cannot be ignored. Therefore, the effect of site variability on the correlated soil properties from collected field data, such as CPT data, obtained from discrete locations across the site needs to be evaluated. In this study, six well-established spatial interpolation techniques — ordinary kriging (OK), simple kriging (SK), universal kriging (UK), inverse distance weight (IDW), spline, and natural neighbor (NaN) — were investigated to evaluate the best interpolation method for incorporating site variability. Six CPT test sites were used to evaluate the performance of these spatial interpretation methods. For each site, CPT profiles at specified locations were generated using the different interpolation techniques, and the generated CPT profiles were compared with the measured CPT profiles. The best-fit line of measured versus predicted cone tip resistance (qc) values, mean bias factor (λ), coefficient of variation (COV), and root mean square error (RMSE) were calculated for each CPT profile and used as a criteria for evaluating the different spatial interpolation methods. The results of this study demonstrated the ability of these spatial interpolation methods for generating CPT profiles with good accuracy. The slope of best-fit line of measured versus predicted qc ranges from 0.93 to 0.95, the mean of λ ranges from 0.90 to 0.98, and the COV ranges from 0.34 to 0.53. The IDW, OK, and SK showed the best spatial interpolation methods (in order) for four out of the six CPT sites. For site 4, OK, IDW, and SK showed the best spatial interpolation methods (in order); while for site 5, the three best spatial interpolation techniques are SK, IDW, and OK (in order).
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