Bayesian Machine Learning in Geotechnical Site Characterization - Brossura

Ching, Jianye

Brossura

ISBN 10: 1032314435 ISBN 13: 9781032314433

Casa editrice: CRC Press, 2025

Riassunto

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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Informazioni sull'autore

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.

Dettagli dell'edizione

• Editore: CRC Press
• Data di pubblicazione: 2025
• Lingua: Inglese
• ISBN 10: 1032314435
• ISBN 13: 9781032314433
• Rilegatura: Copertina flessibile
• Numero edizione: 1
• Numero di pagine: 176
• Contatto del produttore: Taylor and Francis, GPSR Taylor and Francis; gpsr@taylorandfrancis.com; ; Taylor & Francis Verlag GmbH, Kaufingerstraße 24; München; 80331; Germania