Geotechnical design and assessment are often based on data obtained from in-situ or laboratory measurements, e.g., direct data of soil properties or measurements of the geotechnical system performance. Data is also key to the observational method, which is a standard safety verification method in geotechnical engineering. The data can be used to inform model parameters as well as construction and design procedures through application of Bayesian analysis. Thereby, Bayes’ rule enables a consistent combination of data and observations on geotechnical performance with other available information to select proper models, learn the probability distribution of model parameters and update the system reliability estimate. Bayesian updating also facilitates learning the distribution of spatially variable geotechnical properties with sparse measurements and prior knowledge (e.g., engineering experience and judgment that are important in geotechnical practice). This session invites papers that address either methodological developments or novel applications on Bayesian analysis in geotechnical engineering. Individual relevant topics include: Markov chain Monte Carlo methods; sequential Montel Carlo methods; Kriging/Gaussian process models; applications that investigate the influence of prior considerations on the analysis results; definition of the likelihood function; representation of model errors; probabilistic site characterization; reliability updating; the observational method; optimal site investigation and experimental design.
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