Model Uncertainties in Foundation Design is unique in the compilation of the largest and the most diverse load test databases to date, covering many foundation types (shallow foundations, spudcans, driven piles, drilled shafts, rock sockets and helical piles) and a wide range of ground conditions (soil to soft rock).
All databases with names prefixed by NUS are available upon request. This book presents a comprehensive evaluation of the model factor mean (bias) and coefficient of variation (COV) for ultimate and serviceability limit state based on these databases. These statistics can be used directly for AASHTO LRFD calibration.
Besides load test databases, performance databases for other geo-structures and their model factor statistics are provided. Based on this extensive literature survey, a practical three-tier scheme for classifying the model uncertainty of geo-structures according to the model factor mean and COV is proposed. This empirically grounded scheme can underpin the calibration of resistance factors as a function of the degree of understanding – a concept already adopted in the Canadian Highway Bridge Design Code and being considered for the new draft for Eurocode 7 Part 1 (EN 1997-1:202x). The helical pile research in Chapter 7 was recognised by the 2020 ASCE Norman Medal.
Table of Contents
1. Geotechnical Engineering in the Era of Industry 4.0
2. Evaluation and Incorporation of Uncertainties in Geotechnical Engineering
3. Basics in Foundation Engineering
4. Evaluation of Design Methods for Shallow Foundations
5. Evaluation of Design Methods for Offshore Spudcans in Layered Soil
6. Evaluation of Design Methods for Driven Piles and Drilled Shafts
7. Evaluation of Design Methods for Helical Piles
8. Summary and Conclusions
Appendix: Data Availability Statement
Chong Tang is Senior Research Fellow in the Department of Civil and Environmental Engineering at the National University of Singapore.
Kok-Kwang Phoon is Distinguished Professor and Senior Vice Provost for Academic Affairs at the National University of Singapore.
"It provides an insightful discussion on the role of data in the evolution of geotechnical design and situates this discussion within the larger picture of the future of the economy where data is seen as the "new oil" "
-- Jianye Ching in Structural Safety
"The book is written in a manner that is free of jargon and thus removes the mysticism that has dissuaded many practicing engineers to move from classical deterministic treatments of foundation design to a more robust appreciation of margins of safety that considers unavoidable uncertainty in the choice of model geotechnical input parameters and model accuracy."
-- Richard J. Bathurst, in Probabilistic Engineering Mechanics