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Bayesian Machine Learning in Geotechnical Site Characterization

Jianye Ching (National Taiwan University, Taipei)

$315

Hardback

Forthcoming
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English
CRC Press
07 August 2024
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.
By:  
Imprint:   CRC Press
Country of Publication:   United Kingdom
Dimensions:   Height: 234mm,  Width: 156mm, 
ISBN:   9781032314419
ISBN 10:   1032314419
Series:   Challenges in Geotechnical and Rock Engineering
Pages:   176
Publication Date:  
Audience:   College/higher education ,  Professional and scholarly ,  Primary ,  Undergraduate
Format:   Hardback
Publisher's Status:   Forthcoming
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.

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.

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