Machine Learning for Data-Centric Geotechnics, CRC Press, 2025
Editors: Kok-Kwang Phoon, Zi-Jun Cao & Chong Tang
The purpose of this edited book is to share advancements in research and practice that are made possible by machine learning (and other digital technologies) with the goal of accelerating the digital transformation of the geotechnical engineering industry. Numerous foundational challenges beyond methodological ones can be identified when research is pursued under the broader "data first practice central" agenda of data-centric geotechnics.
The book is divided into several parts:
Part 1. Foresight reviews
Part 2. Machine learning and site characterization
Part 3. Machine learning and design
Part 4. Machine learning and construction/operation
Please indicate your preferred part for your chapter.
We would also like our contributors to follow the template below closely:
Introduction
Physical context of the problem
Sufficient information for the practitioner to understand the problem statement
Description of the database
Description of the physical and statistical characteristics of the databases.
An example of a description can be found at:
Ching, J. & Phoon, K. K., "Transformations and Correlations Among Some Clay Parameters - The Global Database", Canadian Geotechnical Journal, 51(6), Jun 2014, 663-685.
Description of the algorithm and its application
For application, it is highly recommended to provide a benchmark example covering training, testing, performance metrics, etc. Highlight challenges such as selection of ad-hoc parameters (if any), type of datasets needed, etc.
An example of a benchmark example can be found at:
Phoon, K. K., Shuku, T., Ching, J. & lkumasa, I., "Benchmark Examples for Data-Driven Site Characterization", Georisk: Assessment & Management of Risk for Engineered Systems & Geohazards, Dec 2022, 16(4), 599-621
Examples and value to practice
Besides correctness, please clearly demonstrate the value to practice using a real project, particularly how it outperforms existing conventional methods in practice.
Conclusions
Database/algorithm availability (mandatory)
Databases/algorithms can be made available under "Support material" at the book website.
Examples are given at:
https://www.routledge.com/Uncertainty-Modeling-and-Decision-Making-in-Geotechnics/Phoon-Shuku-Ching/p/book/9781032367491
Acknowledgments (if any)
References
Appendices (if any)
The manuscript package (including figures, tables, databases, algorithms, permissions, etc.) should be sent to ceetc@dlut.edu.cn by 31 December 2024. The manuscript length (including figures and tables) should be less than 100 pages using double line spacing.