Bayesian Machine Learning in Geotechnical Site Characterization
Bayesian Machine Learning in Geotechnical Site Characterization by Jianye Ching, published by CRC Press in August 2024, presents a comprehensive exploration of Bayesian data analysis and its integration with machine learning techniques. This 176-page book addresses the complexities of geotechnical data, which is often multivariate, sparse, and potentially corrupted, highlighting the limitations of conventional methods in delivering reliable results.
Readers will find a detailed examination of how these advanced methods can be applied across various geotechnical applications, including site characterization, foundation design, and soil property estimation. The book emphasizes the importance of these techniques for large and complex projects, where cost considerations are critical. Through its focus on innovative approaches in technology and engineering, this edition serves as a valuable resource for professionals in the fields of civil engineering and data science.
Official synopsis Publisher
“Bayesian data analysis and modelling linked with machine learning offers a new tool for handling geotechnical data. Geodata is distinctively multivariate, unique, sparse, incomplete, possibly corrupted, and spatial variable, so conventional methods may deliver unreliable results. The powerful methods can be widely applied, as with site characterization, foundation design, underground stratification, soil property estimation, inverse method, observational method, and liquefaction estimation, and are espcially useful for large and complex projects where cost is a major factor”–
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