Probabilistic Analysis Algorithm for UA Slope Software Program

A reliability-based computational algorithm for using a single row and equally spaced drilled shafts to stabilize an unstable slope has been developed in this research. The Monte-Carlo simulation (MCS) technique was used in the previously developed deterministic computational program, in which the limiting equilibrium method of slices was modified to incorporate the arching effects of the drilled shafts in a slope. Uncertainties of soil parameters in the slope were considered by statistical descriptors, including mean, coefficient of variance (c.o.v.), and distribution function. Model errors of the semi-empirical predictive equation for the load transfer factor for characterizing the soil arching effects were considered by statistics of bias. A PC-based research grade program, UA Slope 3.0, was coded to allow for analysis of probability of failure and reliability index of a shaft/slope system. The illustrative example demonstrated that a single value of factor of safety chosen in the deterministic approach may not yield the desired level of reliability as uncertainties of soil parameters and model errors cannot be accounted for systematically. As an extension of this research, importance sampling technique (IST) on drilled shaft/slope system has been proposed to demonstrate its high efficiency, in which the importance function and design point are determined for the ordinary method of slices (OMS) with the accompanying load transfer factor. In addition, the design method of using multiple rows of drilled shaft was developed to stabilize a large slope, in which the design and optimization criteria were proposed to reach the target safety and the constructability while meeting the service limit requirement