Kriging in Slope Reliability Analysis (eBook, ePUB)
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Kriging can be used to determine optimal unbiased predictions for regionalized variables and has been shown to be a powerful tool in slope reliability analysis for reliability-based design. This is the first book to systematically cover the basic theory and applications of the method in slope reliability assessment.The book gives an extensive and detailed presentation of principles and applications, introducing geostatistics and the basic theory of Kriging before addressing the challenges in the application of Kriging in slope reliability analysis. The latest advancements in Kriging applicatio...
Kriging can be used to determine optimal unbiased predictions for regionalized variables and has been shown to be a powerful tool in slope reliability analysis for reliability-based design. This is the first book to systematically cover the basic theory and applications of the method in slope reliability assessment.
The book gives an extensive and detailed presentation of principles and applications, introducing geostatistics and the basic theory of Kriging before addressing the challenges in the application of Kriging in slope reliability analysis. The latest advancements in Kriging application methods are introduced, which enhance computational accuracy and reduce model errors. These include optimization algorithms for spatial parameters in Kriging, adaptive modeling of spatial correlation structures, efficient sampling methods based on Monte Carlo simulation, quantitative analysis of slope failure risks, and reliability analysis methods for unreinforced and reinforced slopes based on conditional random fields. Several case studies are presented to illustrate the practical application and implementation procedures, bridging theory, and practical engineering.
Kriging in Slope Reliability Analysis particularly suits consulting engineers, researchers, and postgraduate students.
The book gives an extensive and detailed presentation of principles and applications, introducing geostatistics and the basic theory of Kriging before addressing the challenges in the application of Kriging in slope reliability analysis. The latest advancements in Kriging application methods are introduced, which enhance computational accuracy and reduce model errors. These include optimization algorithms for spatial parameters in Kriging, adaptive modeling of spatial correlation structures, efficient sampling methods based on Monte Carlo simulation, quantitative analysis of slope failure risks, and reliability analysis methods for unreinforced and reinforced slopes based on conditional random fields. Several case studies are presented to illustrate the practical application and implementation procedures, bridging theory, and practical engineering.
Kriging in Slope Reliability Analysis particularly suits consulting engineers, researchers, and postgraduate students.
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