Databases for Data-Centric Geotechnics
Two Volume Set
1st Edition
Published on 20. December 2024
Book
1056 pages
978-1-032-38873-1 (ISBN)
Description
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.
The first volume pertains to site characterization. The opening chapter presents a deep 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 following 13 chapters then present 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 second volume pertains to geotechnical structures. The opening chapter presents a substantial survey of performance databases and the effectiveness of our prediction models in matching the field measurements in these databases, based on (1) full-scale field tests, (2) 39 prediction exercises organized as a part of international conferences, and (3) comparison between numerical analyses and in-situ or field measurements conducted by the French LCPC. The focus is on the evaluation of the statistical degree of confidence in predicting various of quantities of interest such as capacity and deformation. The following 18 chapters then present databases on the performance of shallow foundations, spudcan foundations, deep foundations, anchors and pipelines, retaining systems and excavations, and landslides.
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, UK and USA.
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.
The first volume pertains to site characterization. The opening chapter presents a deep 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 following 13 chapters then present 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 second volume pertains to geotechnical structures. The opening chapter presents a substantial survey of performance databases and the effectiveness of our prediction models in matching the field measurements in these databases, based on (1) full-scale field tests, (2) 39 prediction exercises organized as a part of international conferences, and (3) comparison between numerical analyses and in-situ or field measurements conducted by the French LCPC. The focus is on the evaluation of the statistical degree of confidence in predicting various of quantities of interest such as capacity and deformation. The following 18 chapters then present databases on the performance of shallow foundations, spudcan foundations, deep foundations, anchors and pipelines, retaining systems and excavations, and landslides.
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, UK and USA.
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.
More details
Series
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
College/higher education
Professional and scholarly
Postgraduate and Professional
Illustrations
222 s/w Abbildungen, 305 farbige Abbildungen, 10 Farbfotos bzw. farbige Rasterbilder, 222 s/w Zeichnungen, 295 farbige Zeichnungen, 284 s/w Tabellen
284 Tables, black and white; 295 Line drawings, color; 222 Line drawings, black and white; 10 Halftones, color; 305 Illustrations, color; 222 Illustrations, black and white
Dimensions
Height: 246 mm
Width: 174 mm
Weight
2110 gr
ISBN-13
978-1-032-38873-1 (9781032388731)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Persons
Kok-Kwang Phoon is Cheng Tsang Man Chair Professor and Provost at the Singapore University of Technology and Design. He was awarded the ASCE Norman Medal twice in 2005 and in 2020, and the Humboldt Research Award in 2017. He is the Founding Editor of Georisk and a Board Member of ISSMGE, and Fellow of the Academy of Engineering Singapore.
Chong Tang is a Professor at Dalian University of Technology in China and former senior research fellow at the National University of Singapore. He was awarded the ASCE Norman Medal in 2020.
Chong Tang is a Professor at Dalian University of Technology in China and former senior research fellow at the National University of Singapore. He was awarded the ASCE Norman Medal in 2020.
Content
Volume 1 (Site Characterization)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.
Volume 2 (Geotechnical Structures)1. Role of performance information in data-centric geotechnics. 2. Variability of predictions for punch-through of foundation in layered soils. 3. Development and use of LCPC pile database. 4. Development and use of CYCU pile load test database. 5. Pile load test database for Southern Africa and evaluation of direct SPT-based pile design methods. 6. Relevant databased pile design approach. 7. Piling insights from a data-centric approach. 8. Bored piles in tropical soils and rocks - a database approach to design. 9. Development and use of databases of pile foundation load tests in Japan. 10. The DINGO database of axial pile load tests for the UK: determination of ultimate load. 11. Development and use of tensile loading test databases for analysis and design of helical piles. 12. Experimental database and data-driven design method of dynamically installed anchors. 13. Pipes and anchors. 14. Mechanically stabilized earth (MSE) walls. 15. Statistical evaluation and calibration of model uncertainty for reliability-based design of soil nail walls. 16. Statistical analyses on a database of deep excavations in Shanghai soft clays. 17. Probabilistic methods for slope failure time prediction.
Volume 2 (Geotechnical Structures)1. Role of performance information in data-centric geotechnics. 2. Variability of predictions for punch-through of foundation in layered soils. 3. Development and use of LCPC pile database. 4. Development and use of CYCU pile load test database. 5. Pile load test database for Southern Africa and evaluation of direct SPT-based pile design methods. 6. Relevant databased pile design approach. 7. Piling insights from a data-centric approach. 8. Bored piles in tropical soils and rocks - a database approach to design. 9. Development and use of databases of pile foundation load tests in Japan. 10. The DINGO database of axial pile load tests for the UK: determination of ultimate load. 11. Development and use of tensile loading test databases for analysis and design of helical piles. 12. Experimental database and data-driven design method of dynamically installed anchors. 13. Pipes and anchors. 14. Mechanically stabilized earth (MSE) walls. 15. Statistical evaluation and calibration of model uncertainty for reliability-based design of soil nail walls. 16. Statistical analyses on a database of deep excavations in Shanghai soft clays. 17. Probabilistic methods for slope failure time prediction.