ZOU Hai-feng, CAI Guo-jun, LIU Song-yu, et al. Research on modeling inherent spatial variability of piezocone penetration
test cone tip resistance based on geostatistics[J]. 2015, 36(S1): 403-407.
DOI:
ZOU Hai-feng, CAI Guo-jun, LIU Song-yu, et al. Research on modeling inherent spatial variability of piezocone penetration
test cone tip resistance based on geostatistics[J]. 2015, 36(S1): 403-407.DOI: 10.16285/j.rsm.2015.S1.070.
Research on modeling inherent spatial variability of piezocone penetration
test cone tip resistance based on geostatistics
Geostatistics is one of the most powerful techniques for modeling the inherent spatial variability of soil properties. In geostatistics
the semivariogram is utilized to describe the continuous variation characteristics and then the Kriging interpolation is applied to provide the best linear unbiased estimates of soil properties. Geostatistics is used to model the inherent spatial variability of piezocone penetration test(CPTU) cone tip resistance qt in a testing section of Suqian-Xinyi Expressway for site characterization. First of all
theoretical background of the geostatistics involving constructing experimental semivariogram
theoretical semivariogram fit and Kriging prediction is reviewed briefly. Least square regression analysis is performed to remove the linear trend function within the qt data to generate the weak stationary residuals. It is found that exponential theoretical semivariogram can accurately describe the correlation structure and variability of the residuals at this site. Significant anisotropy is observed in the correlation structure of qt residuals. Results show that the vertical range is about 1.2 m while the horizontal range is 4.05 m for qt at this site. The distribution map of qt data at the target site is obtained using ordinary Kriging interpolation and compared with in-situ measurement data. It is concluded that the Kriging estimates are comparable to the measurements at this site. However
the reliability of estimates may decrease at the locations corresponding to peak values and those far away from observations. This research can improve the reliability of geotechnical design in practice since the inherent spatial variability of qt data is accurately evaluated.