Abstract: Benchmarking is very valuable for evaluating and comparing methodologies. Here, Gaussian process regression using multiple Gaussian random fields (GPR-MR) is applied to benchmarking data for spatial variability problems. ...

Abstract: Two issues are focused on: respectively, a problem of sequential updating with plural sets of data, and a problem of optimal allocation of observation points within the scope of conditional stochastic fields. Concerning ...

Abstract: A general formulation is presented based on the maximum likelihood method to identify the best estimator of a stochastic Gaussian field when the observation is made at discrete spatial points. The uncertainty of the estimator ...

Abstract: Data assimilation with a particle filter (PF) has attracted attention for use in Bayesian updating. However, PFs have a problem known as degeneracy, where weights tend to concentrate into only a few particles after a few ...

Abstract: We propose a method for simultaneously estimating the trend and random components of soil properties using the least absolute shrinkage and selection operator (LASSO) for sparse modeling without assuming any basis functions ...

Abstract: Many researchers have developed bridge weigh-in-motion (BWIM) technology, mainly focusing on the representative value of the estimated axle weights. However, the estimation of the probabilistic distribution of axle weights ...

Abstract: This paper compiles a large cone penetration test (CPT) database, named CPT3D/3/2577, to investigate the horizontal and vertical autocovariance structures of soil spatial variation. It contains three CPT parameters (cone ...