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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Bayesian Machine Learning in Geotechnical Site Characterization

Bayesian Machine Learning in Geotechnical Site Characterization

Ching, Jianye

Paperback

Series: Challenges in Geotechnical and Rock Engineering

Technology & EngineeringGeneral Computers

PREORDER - Expected ship date December 26, 2025

ISBN10: 1032314435
ISBN13: 9781032314433
Publisher: CRC Press
Published: Dec 26 2025
Pages: 176
Weight: 0.60
Height: 0.40 Width: 6.14 Depth: 9.21
Language: English

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.

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