Bayesian Prediction of Punch-Through Probability for Spudcans in Stiff-over-Soft Clay

This paper illustrates a Bayesian approach to the estimation of the prior and in-operation probability of punch-through of spudcans in stiff-over-soft clay stratigraphies. Prior probabilities of occurrence of punch-through (i.e., before installation) are estimated spatially for couples of depth-load values based on probabilistic implementation of analytical models obtained from LDFE (Large Deformation Finite Element) analyses. Subsequently, in the course of the installation process, monitoring data are used to update probability values on the basis of load-displacement curves which are presumable on the basis of geotechnical and geometric features of the soil-structure system. Load-displacement behavior is also characterized based on results of LDFE tests. The approach allows the quantitative modeling of uncertainties in geometric and geotechnical parameters, as well as in the presumable load-displacement behavior. Such uncertainties are addressed explicitly, parameterized probabilistically through the selection of suitable probability distributions and propagated in the reference analytical models for estimating punch-through depth, peak load and load-displacement behavior using Monte Carlo simulation. The Bayesian approach allows both the preliminary and the observational estimation of punch-through probability, thus enabling a more rational decision-making process (both in the design and in-operation phases) through the comparison of probability values with a pre-established minimum tolerable threshold. A practical application of the method is provided through the illustration of a fully worked example case-study.