Multivariate correlation among resilient modulus and cone penetration test parameters of cohesive subgrade soils

Extensive research has been conducted to establish empirical equations between the resilient modulus (M-r) and other testing parameters. Despite an increase in the correlation studies, less effort has been made in developing reliable correlations to predict the resilient modulus using cone penetration testing indices. In this research, an attempt has been made to propose new correlations between M-r and the piezocone penetration test (CPTU) indices of clayey soils by using a multivariate normal distribution approach. A database collected from 16 different sites in Jiangsu province, China, was presented to achieve this study. The database contains 124 sets of resilient modulus (M-r) values at the in-situ stress condition, cone tip resistance (q(c)), sleeve frictional resistance (f(s)), moisture (w) and dry density (gamma(d)). Four major procedures including the data transformation, correlation analysis, Bayesian updating, and back transformation were applied to derive the correlations among M-r and other indices in the framework of the multivariate normal distribution model. First, each individual parameter was converted to a standard normal variable using the Box-Cox transformation. Later, the correlation matrix of the multivariate normal distribution model was estimated using the product-moment (Pearson) correlation coefficients between all pairwise data. The uncertainties associated with the Box-Cox transformation parameters and the correlation coefficients were evaluated using a bootstrapping technique. Furthermore, the formulas for predicting the posterior mean, coefficient of variation (COV), median, and 95% confidence interval (CI) of Mr conditional on different indices were derived using Bayesian updating combined with back transformation. A new approach based on the Taylor-series expansion was proposed to approximate these statistics in this study. Comparisons between the correlations derived from the multivariate model, the database of Jiangsu clay, and the data collected from the literature demonstrated that the model should be capable of the correlations among the five indices, but it might be slightly biased when applied in the global dataset This research highlighted a new method to establish the reliable correlations to update Mr using different testing indices. (C) 2016 Elsevier B.V. All rights reserved.