Spatial variability of undrained strength (Cu) has been modelled in several ways in the past. In particular, concepts of time series such as autoregressive moving average models have been used to model the analogous "spatial series" of the values of depth versus undrained strength. It should be noted that the very purpose of such modelling studies is to provide estimates of the values of undrained strength at a given value of depth. In the present paper, the main prerequisite to apply these models, viz. the complete removal of trend present in the spatial series of depth versus Cu, has been focussed. An accurate modelling procedure is recommended which can estimate the values of Cu at a given value of depth better than any other model in this class of models existing in the literature. Sensitivity in the trend patterns of the depth versus Cu data is well taken care of. A computer program has been developed in FORTRAN 77to fit the model in conjunction with a standard nonlinear least-squares routine taken from the literature. One of the advantages of the present model is the speed of convergence of the computer program. Two case studies appearing in the literature have been successfully solved to demonstrate the efficacy of the model developed. Key words : spatial variability, time series analysis, spatial series, nonstationarity, autoregressive moving average models, regression, nonlinear least squares, error sum of squares.
Censored time series analysis with autoregressive moving average models
