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Bayesian Machine Learning in Geotechnical Site Characterization (Challenges in Geotechnical and Rock Engineering) - Hardcover

Ching, Jianye

 
9781032314419: Bayesian Machine Learning in Geotechnical Site Characterization (Challenges in Geotechnical and Rock Engineering)
Hardcover
ISBN 10: 1032314419 ISBN 13: 9781032314419
Verlag: CRC Press, 2024

Inhaltsangabe

Bayesian data analysis and modelling linked with machine learning offers a new tool for handling geotechnical data. This book presents recent advancements made by the author in the area of probabilistic geotechnical site characterization.

Two types of correlation play central roles in geotechnical site characterization: cross-correlation among soil properties and spatial-correlation in the underground space. The book starts with the introduction of Bayesian notion of probability “degree of belief”, showing that well-known probability axioms can be obtained by Boolean logic and the definition of plausibility function without the use of the notion “relative frequency”. It then reviews probability theories and useful probability models for cross-correlation and spatial correlation. Methods for Bayesian parameter estimation and prediction are also presented, and the use of these methods demonstrated with geotechnical site characterization examples.

Bayesian Machine Learning in Geotechnical Site Characterization suits consulting engineers and graduate students in the area.

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Über die Autorin bzw. den Autor

Jianye Ching is Distinguished Professor at National Taiwan University and Convener of the Civil & Hydraulic Engineering Program of the Ministry of Science and Technology of Taiwan. He is Chair of ISSMGE‘s TC304 (risk), Chair of Geotechnical Safety Network (GEOSNet), and Managing Editor of the journal Georisk.

Von der hinteren Coverseite

This presents recent advancements in probabilistic geotechnical site characterization. It reviews probability theories and models for cross correlation and spatial correlation, and presents methods for Bayesian parameter estimation and prediction. Use of these methods is demonstrated with geotechnical site characterization examples.

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Verlag
CRC Press
Erscheinungsdatum
2024
Sprache
Englisch
ISBN 10 
1032314419
ISBN 13 
9781032314419
Einband
Gebundene Ausgabe
Auflage
1
Anzahl der Seiten
176
Kontakt zum Hersteller
preigu GmbH & Co. KG
mail@preigu.de

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