Please use this identifier to cite or link to this item: https://doi.org/10.1061/(ASCE)1090-0241(2003)129:7(649)
Title: Identification of statistically homogeneous soil layers using modified bartlett statistics
Authors: Phoon, K.-K. 
Quek, S.-T. 
An, P.
Keywords: Correlation
Homogeneity
Monte Carlo method
Random processes
Soil layers
Stationary processes
Statistics
Issue Date: Jul-2003
Source: Phoon, K.-K., Quek, S.-T., An, P. (2003-07). Identification of statistically homogeneous soil layers using modified bartlett statistics. Journal of Geotechnical and Geoenvironmental Engineering 129 (7) : 649-659. ScholarBank@NUS Repository. https://doi.org/10.1061/(ASCE)1090-0241(2003)129:7(649)
Abstract: Stationarity or statistical homogeneity is an important prerequisite for subsequent statistical analysis on a given section of a soil profile to be valid. The estimation of important soil statistics such as the variance is likely to be biased if the profile is not properly demarcated into stationary sections. Existing classical statistical tests are inadequate even for simple identification of stationarity in the variance because the spatial variations of soil properties are generally correlated with each other. In this paper, a modified Bartlett statistical test is proposed to provide a more rational basis for rejecting the null hypothesis of stationarity in the correlated case. The accompanying rejection criteria are determined from simulated correlated sample functions and summarized into a convenient form for practical use. A statistical-based soil boundary identification procedure is then developed using the modified Bartlett test statistic. Based on the analysis of a piezocone sounding record, two advantages can be discerned. First, the proposed procedure provides a useful supplement to existing empirical soil classification charts, especially in situations where inherent variability tends to complicate interpretation of soil layers. Second, various key assumptions in geostatistical analysis such as stationarity and choice of trend function can be verified more rigorously using the framework of hypothesis testing.
Source Title: Journal of Geotechnical and Geoenvironmental Engineering
URI: http://scholarbank.nus.edu.sg/handle/10635/65679
ISSN: 10900241
DOI: 10.1061/(ASCE)1090-0241(2003)129:7(649)
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