Databases for Data-Centric Geotechnics: Site Characterization
Databases for Data-Centric Geotechnics forms a definitive reference and guide to databases in geotechnical and rock engineering, to enhance decision-making in geotechnical practice using data-driven methods. This first volume pertains to site characterization. The opening chapter presents an in-depth analysis of site data attributes, including the establishment of a new taxonomy of site data under “4S” (site generalizations, spatial features, sampling characteristics, and smart data) to provide a novel agenda for data-driven site characterization. Type 3 machine learning methods (disruptive value) are possible as sensors become more pervasive and more intelligent. A comprehensive overview of site characterization information is also presented with a focus on its availability, coverage, value to decision making, and challenges. The remaining 13 chapters cover databases of soil and rock properties and the application of these databases to rock socket behavior, rock classification, settlement on soft marine clays, permeability of fine-grained soils, and liquefaction among others. The databases were compiled from studies undertaken in many countries including Austria, Australia, Brazil, Canada, China, France, Finland, Germany, India, Iran, Japan, Korea, Malaysia, Mexico, New Zealand, Norway, Singapore, Sweden, Thailand, the United Kingdom, and the United States.

This volume on site characterization is a companion to the volume on geotechnical structures. Databases for Data-Centric Geotechnics represents the most diverse and comprehensive assembly of database research in a single publication (consisting of two volumes) to date. It follows from Model Uncertainties for Foundation Design, also published by CRC Press, and suits specialist geotechnical engineers, researchers and graduate students.

Chapter 6 of this book is freely available as a downloadable Open Access PDF at http://www.taylorfrancis.com under a Creative Commons [(CC BY)] 4.0 license.

1145642927
Databases for Data-Centric Geotechnics: Site Characterization
Databases for Data-Centric Geotechnics forms a definitive reference and guide to databases in geotechnical and rock engineering, to enhance decision-making in geotechnical practice using data-driven methods. This first volume pertains to site characterization. The opening chapter presents an in-depth analysis of site data attributes, including the establishment of a new taxonomy of site data under “4S” (site generalizations, spatial features, sampling characteristics, and smart data) to provide a novel agenda for data-driven site characterization. Type 3 machine learning methods (disruptive value) are possible as sensors become more pervasive and more intelligent. A comprehensive overview of site characterization information is also presented with a focus on its availability, coverage, value to decision making, and challenges. The remaining 13 chapters cover databases of soil and rock properties and the application of these databases to rock socket behavior, rock classification, settlement on soft marine clays, permeability of fine-grained soils, and liquefaction among others. The databases were compiled from studies undertaken in many countries including Austria, Australia, Brazil, Canada, China, France, Finland, Germany, India, Iran, Japan, Korea, Malaysia, Mexico, New Zealand, Norway, Singapore, Sweden, Thailand, the United Kingdom, and the United States.

This volume on site characterization is a companion to the volume on geotechnical structures. Databases for Data-Centric Geotechnics represents the most diverse and comprehensive assembly of database research in a single publication (consisting of two volumes) to date. It follows from Model Uncertainties for Foundation Design, also published by CRC Press, and suits specialist geotechnical engineers, researchers and graduate students.

Chapter 6 of this book is freely available as a downloadable Open Access PDF at http://www.taylorfrancis.com under a Creative Commons [(CC BY)] 4.0 license.

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Databases for Data-Centric Geotechnics: Site Characterization

Databases for Data-Centric Geotechnics: Site Characterization

Databases for Data-Centric Geotechnics: Site Characterization
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Databases for Data-Centric Geotechnics: Site Characterization

Databases for Data-Centric Geotechnics: Site Characterization

Overview

Databases for Data-Centric Geotechnics forms a definitive reference and guide to databases in geotechnical and rock engineering, to enhance decision-making in geotechnical practice using data-driven methods. This first volume pertains to site characterization. The opening chapter presents an in-depth analysis of site data attributes, including the establishment of a new taxonomy of site data under “4S” (site generalizations, spatial features, sampling characteristics, and smart data) to provide a novel agenda for data-driven site characterization. Type 3 machine learning methods (disruptive value) are possible as sensors become more pervasive and more intelligent. A comprehensive overview of site characterization information is also presented with a focus on its availability, coverage, value to decision making, and challenges. The remaining 13 chapters cover databases of soil and rock properties and the application of these databases to rock socket behavior, rock classification, settlement on soft marine clays, permeability of fine-grained soils, and liquefaction among others. The databases were compiled from studies undertaken in many countries including Austria, Australia, Brazil, Canada, China, France, Finland, Germany, India, Iran, Japan, Korea, Malaysia, Mexico, New Zealand, Norway, Singapore, Sweden, Thailand, the United Kingdom, and the United States.

This volume on site characterization is a companion to the volume on geotechnical structures. Databases for Data-Centric Geotechnics represents the most diverse and comprehensive assembly of database research in a single publication (consisting of two volumes) to date. It follows from Model Uncertainties for Foundation Design, also published by CRC Press, and suits specialist geotechnical engineers, researchers and graduate students.

Chapter 6 of this book is freely available as a downloadable Open Access PDF at http://www.taylorfrancis.com under a Creative Commons [(CC BY)] 4.0 license.

Product Details

ISBN-13: 9781032578958
Publisher: CRC Press
Publication date: 12/20/2024
Series: Challenges in Geotechnical and Rock Engineering
Pages: 512
Product dimensions: 6.88(w) x 9.69(h) x (d)

About the Author

Kok-Kwang Phoon is Cheng Tsang Man Chair Professor and President of the Singapore University of Technology and Design. He was awarded the ASCE Norman Medal twice in 2005 and 2020, the Humboldt Research Award in 2017, and the ASCE Alfredo Ang Award in 2024. He is the Founding Editor of Georisk and Geodata and AI. He is a Fellow of the Academy of Engineering Singapore and Singapore National Academy of Science.

Chong Tang is Professor at Dalian University of Technology in China and a former senior research fellow at the National University of Singapore. He was awarded the ASCE Norman Medal in 2020.

Table of Contents

1. Role of site characterization information in data-centric geotechnics. 2. Selection of rock hydromechanical parameters for rock foundation
design: a database approach. 3. Evaluation of soil/rock properties using databases. 4. Undrained shear strength of Finnish soft clays: a database perspective. 5. Role of databases in the evaluation of soil properties. 6. New laboratory database of hydraulic conductivity measurements on fine-grained soils. 7. Normalised active undrained shear strengths of soft Scandinavian clays – a data-centric and a geomechanical approach. 8. Prediction for the mechanical response of gravels. 9. Evaluation of compressibility properties for soft marine clays. 10. An engineering geological parameter database of tunnel surrounding rock and its application. 11. Exploring challenges via analysis of multivariate geotechnical proper-ties: insights from large-scale local sampling of Japanese marine clay. 12. In situ test-based evaluation of soil effective stress strength properties and stress history. 13. Mechanical-statistical evaluation of soil properties. 14. Data-centric seismic soil liquefaction assessment: approaches, data, and tools. 15. Prediction for soil design properties based on a multivariate database for Shanghai soft clay.

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