Assessment of Different Spatial Interpolation Techniques for Generating Synthetic Soil Boring Data

Abstract

Subsurface soil conditions usually involve special site variability that cannot be ignored for design and analysis. Therefore, the effect of site variability on associated soil properties should be assessed using gathered field data, such as soil boring data collected from discrete locations. In this study, six spatial interpolation techniques, the ordinary kriging (OK), simple kriging (SK), universal kriging (UK), inverse distance weight (IDW), spline, and natural neighbor (NaN) were evaluated to assess the best prediction strategy for considering site variability. The efficacy of these methods was tested at four soil boring sites. Boring profiles were generated using the different techniques at specified locations for each site, and the created data were compared with the measured soil boring profiles. For each location, the best-fit line of measured versus predicted undrained shear strength (S_u) or standard penetration test (SPT) number, mean bias factor (λ), coefficient of effectiveness (COE), root mean square error (RMSE), and coefficient of variation (COV), were calculated and used to assess the various interpolation methods. The findings of this study demonstrated the ability of these spatial interpolations to produce precise soil boring data. The slope of best-fit line of measured/generated S_u and SPT ranged from 0.89 to 0.99. The best-performing interpolation methods (in order) are: IDW, OK/UK, and SK methods. The results show that the COVs between the measured and synthetic soil boring data at the selected points are significantly lower than the COVs between the measured soil boring profiles for the entire site.