Databases for Data-Centric Geotechnics

Site Characterization
1. Auflage 2024
ISBN: 978-1-032-57895-8
Verlag: Taylor & Francis Ltd (Sales)

Buch, Englisch, 512 Seiten, Format (B × H): 170 mm x 244 mm, Gewicht: 1007 g

Reihe: Challenges in Geotechnical and Rock Engineering

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.

Zielgruppe

Postgraduate and Professional Reference

Autoren/Hrsg.

Phoon, Kok-Kwang

Tang, Chong

Fachgebiete

• Technische Wissenschaften Bauingenieurwesen Bauingenieurwesen
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Mathematik | Informatik EDV | Informatik Informatik Theoretische Informatik
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken
• Technische Wissenschaften Bauingenieurwesen Boden- und Felsmechanik, Geotechnik

Inhaltsverzeichnis

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.

Über Autor(innen)

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.