Marine industry requires continued development of new technologies in order to produce oil. An essential requirement in design is to be able to compare experimental data from prototype structures with predicted information from a corresponding analytical finite element model. In this study, structural model updating may be defined as the fit of an existing analytical model in the light of measu...

in this current study a generalized super-efficiency model is first proposed for ranking extreme efficient decision making units (dmus) in stochastic data envelopment analysis (dea) and then, a deterministic (crisp) equivalent form of the stochastic generalized super-efficiency model is presented. it is shown that this deterministic model can be converted to a quadratic programming model. so fa...

In practical engineering, experimental data is not fully in line with the true system response due to various uncertain factors, e.g., parameter imprecision, model uncertainty, and measurement errors. presence of mixed sources aleatory epistemic stochastic updating a powerful tool for validation calibration. This paper investigates use Bray-Curtis (B-C) distance proposes Bayesian approach addre...

Stochastic approximation Monte Carlo (SAMC) has recently been proposed by Liang, Liu and Carroll [J. Amer. Statist. Assoc. 102 (2007) 305–320] as a general simulation and optimization algorithm. In this paper, we propose to improve its convergence using smoothing methods and discuss the application of the new algorithm to Bayesian model selection problems. The new algorithm is tested through a ...

Stochastic approximation Monte Carlo (SAMC) has recently been proposed by Liang, Liu and Carroll (2007) as a general simulation and optimization algorithm. In this paper, we propose to improve its convergence using smoothing methods and discuss the application of the new algorithm to Bayesian model selection problems. The new algorithm is tested through a change-point identification example. Th...

This paper presents a novel image-based approach for updating the geometry of 3D models. The technique can cope with large-scale models, using a single imaging sensor to which an arbitrary motion is applied. Current approaches usually do not fully take advantage of strong prior information, often available in the form of an initial model. The approach is thus novel in that geometric anomalies a...

  Data envelopment analysis (DEA) is a nonparametric approach to evaluate theefficiency of decision making units (DMU) using mathematical programmingtechniques. Almost, all of the previous researches in stochastic DEA have been usedthe stochastic data when the inputs and outputs are normally distributed. But, thisassumption may not be true in practice. Therefore, using a normal distribution wi...

Abstract The nonparametric probabilistic method (NPM) for modeling and quantifying model-form uncertainties is a physics-based, computationally tractable, machine learning performing uncertainty quantification model updating. It extracts from data information not captured by deterministic, high-dimensional (HDM) of dimension N infuses it into counterpart stochastic, hyperreduced, projection-bas...