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Reliability analysis with stratified importance sampling based on adaptive Kriging

Reliability Engineering & System Safety, 2020
In reliability engineering, estimating the failure probability of a system is one of the most challenging tasks. Since many applied engineering tasks are computationally expensive, it is challenging to estimate failure probabilities using acceptable ...
Sinan Xiao, S. Oladyshkin, W. Nowak
semanticscholar   +1 more source

Spatial distribution of the groundwater quality using kriging and Co-kriging interpolations

, 2020
The present work is aimed for investigation the groundwater quality for drinking purposes in El Mila plain, Algeria. This is carried out through an integrated approach of groundwater quality index (GWQI) and geostatistical method for mapping this index ...
Lazhar Belkhiri, Ammar Tiri, Lotfi Mouni
semanticscholar   +1 more source

Geographical detector-based stratified regression kriging strategy for mapping soil organic carbon with high spatial heterogeneity

, 2021
Spatial variability of soil organic carbon (SOC) content is crucial in precision fertilization and climate change prediction. Stratified ordinary kriging (SOK), which considers the sharp spatial variability of SOC due to natural factors and human ...
Yaolin Liu   +4 more
semanticscholar   +1 more source

Mapping soil organic matter contents at field level with Cubist, Random Forest and kriging

Geoderma, 2019
The soil organic matter (SOM) content is strongly related to soil fertility and greenhouse gas emissions. Knowledge of the soil SOM content is therefore necessary for efficient and sustainable management practices.
N. Pouladi   +3 more
semanticscholar   +1 more source

Combining Kriging meta models with U-function and K-Means clustering for prediction of fracture energy of concrete

, 2021
In this study, a combination of Kriging surrogate method with U-learning function and K-means clustering were used to predict the concrete fracture energy (as an output parameter) based on previous experimental data sets including compressive strength ...
Iman Afshoon, M. Miri, S. Mousavi
semanticscholar   +1 more source

Disjunctive kriging, universal kriging, or no kriging: Small sample results with simulated fields

Mathematical Geology, 1986
This paper provides a comparison between linear (universal) and nonlinear (disjunctive) kriging estimators when they are computed from small samples chosen randomly on simulated stationary and nonstationary fields. Point estimation results are reported.
Carlos E. Puente, Rafael L. Bras
openaire   +1 more source

Topology optimization using material-field series expansion and Kriging-based algorithm: An effective non-gradient method

, 2020
Topology optimization is now a very effective and important tool for designing the layouts of various structural and multidisciplinary problems, but most existing methods require information about the sensitivity of the performance function with respect ...
Yangjun Luo, Jian Xing, Z. Kang
semanticscholar   +1 more source

A novel learning function based on Kriging for reliability analysis

Reliability Engineering & System Safety, 2020
Adaptively constructing the surrogate model for reliability analysis has been widely studied for the advantage of guaranteeing the estimation accuracy while calling the real performance function as little as possible.
Yan Shi   +4 more
semanticscholar   +1 more source

Universal Kriging

2019
This chapter discusses universal kriging, the kriging of a random function Z(x) which is not intrinsic and exhibits an expectation E [Z(x)] = m(x) variable over the space. For the function m(x), called the drift of Z(x), a model has to be chosen, usually polynomial of degree 1, 2, or 3.
Guojun Gan, Emiliano A. Valdez
openaire   +2 more sources

Area Influence Kriging

Mathematical Geology, 2002
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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