A statistical approach for generating synthetic tip stress data from limited CPT soundings
CPT tip stress data obtained from a Uranium mill tailings impoundment are treated as time series. A statistical class of models that was developed to model time series is explored to investigate its applicability in modeling the tip stress series. These models were developed by Box and Jenkins (1970) and are known as Autoregressive Moving Average (ARMA) models. This research demonstrates how to apply the ARMA models to tip stress series. Generation of synthetic tip stress series that preserve the main statistical characteristics of the measured series is also investigated. Multiple regression analysis is used to model the regional variation of the ARMA model parameters as well as the regional variation of the mean and the standard deviation of the measured tip stress series. The reliability of the generated series is investigated from a geotechnical point of view as well as from a statistical point of view. Estimation of the total settlement using the measured and the generated series subjected to the same loading condition are performed. The variation of friction angle with depth of the impoundment materials is also investigated. This research shows that these series can be modeled by the Box and Jenkins ARMA models. A third degree Autoregressive model AR(3) is selected to represent these series. A theoretical double exponential density function is fitted to the AR(3) model residuals. Synthetic tip stress series are generated at nearby locations. The generated series are shown to be reliable in estimating the total settlement and the friction angle variation with depth for this particular site.
- Research Organization:
- Colorado State Univ., Fort Collins, CO (United States)
- OSTI ID:
- 7099332
- Country of Publication:
- United States
- Language:
- English
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Related Subjects
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE WASTES FROM NUCLEAR FACILITIES
DEPTH
DIMENSIONS
FRICTION FACTOR
MATHEMATICAL MODELS
MATHEMATICS
MILL TAILINGS
MINES
REGRESSION ANALYSIS
SOLID WASTES
SPOIL BANKS
STATISTICAL MODELS
STATISTICS
STRESS ANALYSIS
TAILINGS
UNDERGROUND FACILITIES
URANIUM MINES
WASTES