Bayesian Machine Learning in Geotechnical Site Characterization
1st Edition
Will be published approx. on 26. December 2025
Book
Paperback/Softback
176 pages
978-1-032-31443-3 (ISBN)
Description
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.
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.
More details
Series
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
College/higher education
Professional and scholarly
Academic, Postgraduate, and Professional
Illustrations
78 s/w Zeichnungen, 14 s/w Tabellen, 78 s/w Abbildungen
14 Tables, black and white; 78 Line drawings, black and white; 78 Illustrations, black and white
Dimensions
Height: 234 mm
Width: 156 mm
Thickness: 10 mm
Weight
298 gr
ISBN-13
978-1-032-31443-3 (9781032314433)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
E-Book
08/2024
1st Edition
CRC Press
€97.49
Available for download
E-Book
08/2024
1st Edition
CRC Press
€97.49
Available for download
Book
08/2024
1st Edition
CRC Press
€241.50
Shipment within 10-20 days
Person
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.
Content
1. Bayesian Approach. 2. Review of Probability and Models. 3. Bayesian Parameter Estimation and Prediction. 4. Geotechnical Data and Bayesian Modeling. 5. Full-scale Real Case Study.