Databases for Data-Centric Geotechnics

Geotechnical Structures
1. Auflage 2024
ISBN: 978-1-032-57910-8
Verlag: CRC Press

Buch, Englisch, 690 Seiten, Format (B × H): 170 mm x 244 mm, Gewicht: 1288 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 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 such as Australia, Belgium, Bolivia, Brazil, Canada, China, Egypt, France, Germany, Hungary, Iran, Ireland, Japan, Kenya, Malaysia, Netherlands, Norway, Poland, Portugal, South Africa, the United Kingdom and the United States.

This volume on geotechnical structures is a companion to the volume on site characterization. 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.

Tang, Chong

Phoon, Kok-Kwang

Fachgebiete

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

Inhaltsverzeichnis

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.

Über Autor(innen)

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 2020, and the Humboldt Research Award in 2017. He is the Founding Editor of Georisk, 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.