Geotechnical engineers are always faced with uncertainties and spatial variations in material parameters. In this work, we propose to develop a framework able to account for different types of uncertainties in a formal and logical manner, to incorporate all available sources of information, and to integrate the uncertainty in an estimate of the probability. In geotechnical engineering, current soil classification charts based on CPT data may not provide an accurate prediction of soil type, even though soil classification is an essential component in the design process. As a cheaper and faster alternative to sample retrieval and testing, field methods such as the cone penetration test (CPT) can be used. A probabilistic soil classification approach is proposed here to improve soil classification based on CPT. The proposed approach provides a simple and straightforward tool that allows updating the soil classification charts based on sitespecific data. In general, settlements can be the result of surface loads or variable soil deposits. In current practice, the analysis to determine settlements is deterministic. It assumes that the soil profile at a site is uniform from location to location, and only allows limited consideration of the variations of the material properties and initial conditions within soil layers in spite of the wide range of compositions, gradations, and water contents in natural soils. A Bayesian methodology is used to develop an unbiased probabilistic model that accurately predicts the settlements and accounts for all the prevailing uncertainties. The proposed probabilistic model is used to estimate the settlements of the foundation of a structure in the Venice Lagoon, Italy. The conditional probability (fragility) of exceeding a specified settlement threshold for a given vertical pressure is estimated. A predictive fragility and confidence intervals are developed with special attention given to the treatment and quantification of aleatory and epistemic uncertainties. Sensitivity and importance measures are computed to identify the key parameters and random variables in the model.

One of the most critical problems geotechnical engineers face with is the determination of the amount of consolidation settlement that will occur at a site as a result of the construction of a structure. The compressibility behavior of the soil is an important parameter in determining the amount of consolidation settlement. The goal of this study is to develop probabilistically based correlation between the compressibility behavior of soil and in-situ test data. Within the scope of this research effort, performed CPT tests and the recorded settlement case histories where consolidation settlements at the field under various surcharge loads were compiled from the Bursa East and West Waste Water Treatment Plant soil investigation projects. A database was composed of the results of 45 CPT and 57 settlement plate recordings. For the compilation of this database, a series of finite difference software FLAC-3D analyses were carried out to calculate the change in stress distribution under the settlement plates. A maximum likelihood framework was used for the development of compressibility behavior of soils. As a result of careful processing of available data, the cone tip resistance (qc), soil behavior type index (Ic) were selected as two important parameters effecting the value of the one-dimensional constraint modulus, M. The regression analysis which uses the settlement values recorded at the site and those computed using the change in the stress distribution, the thickness of the sublayers and the proposed one-dimensional constraint modulus were carried out to calculate the values of these model parameters. Two correlations based on the cone tip resistance and soil behavior type index were developed for the computation of the one-dimensional constraint modulus, M.