Abstract: It is important to be able to construct a site-specific multivariate probability density function (PDF) of soil parameters based on limited and incomplete site-specific investigation data alone. This allows the unique ...

Abstract: This paper presents a newly developed simulation-based approach for Bayesian model updating, model class selection, and model averaging called the transitional Markov chain Monte Carlo (TMCMC) approach. The idea behind ...

Abstract: A novel three-step approach is proposed to solve reliability-based optimization (RBO) problems. The new approach is based on a novel approach previously developed by the authors for estimating failure probability functions. ...

Abstract: This paper proposes a Bayesian method of measuring the similarity between a set of site-specific data and individual records in a generic geotechnical database. All data are assumed to be multivariate, uncertain, unique, ...

Abstract: This study proposes a novel data-driven Bayesian machine learning method for constructing site-specific multivariate probability distribution models in geotechnical engineering. There is a trade-off for constructing a ...

Abstract: The scale of fluctuation (SOF) of a spatially variable soil property has been known to be the most important parameter that characterizes the effect of spatial averaging, whereas the type of the autocorrelation model ...

Abstract: In this paper, the sparse Bayesian learning (SBL) approach previously proposed for the characterization of one-dimensional (1D) soil spatial variability is extended to a more realistic three-dimensional (3D) setting. Direct ...

Abstract: In geotechnical engineering, it is challenging to construct a site-specific multivariate probability distribution model for soil/rock properties because the site-specific data are usually sparse and incomplete. In contrast, ...