The story of statistics in geotechnical engineering

ABSTRACT

The story of statistics in geotechnical engineering can be traced to Lumb’s classical Canadian Geotechnical Journal paper on “The Variability of Natural Soils” published in 1966. In parallel, the story of risk management in geotechnical engineering has progressed from design by prescriptive measures that do not require site-specific data, to more refined estimation of site-specific response using limited data from site investigation as inputs to physical models, to quantitative risk assessment (QRA) requiring considerable data at regional/national scales. In an era where data is recognised as the “new oil”, it makes sense for us to lean towards decision making strategies that are more responsive to data, particularly if we have zettabytes coming our way. In fact, we already have a lot of data, but the vast majority is shelved after a project is completed (“dark data”). It does not make sense to reduce one zettabyte to a few bytes describing a single cautious value. It does not make sense to expect big data to be precise and to fit a particular favourite physical model as demanded by the classical deterministic world view. This paper advocates the position that there is value in data of any kind (good or not so good quality, or right or wrong fit to a physical model) and the challenge is for the new generation of researchers to uncover this value by hearing what data have to say for themselves, be it using probabilistic, machine learning, or other data-driven methods including those informed by physics and human experience, and to re-imagine the role of the geotechnical engineer in an immersive environment likely to be imbued by machine intelligence.

KEYWORDS:

• Big Indirect Data (BID)
• generic databases
• MUSIC-X
• transformation models
• site challenge
• similarity index

Acknowledgements

This paper is an update of the 10th Lumb Lecture, delivered at the University of Hong Kong, 6 December 2018. The author would like to thank Professor Limin Zhang, Editor in Chief of Georisk, for his encouragement to prepare this paper. The author is also grateful to the Department of Civil Engineering, The University of Hong Kong and the Geotechnical Division, The Hong Kong Institution of Engineers for their kind invitation to deliver this lecture. In particular, the generous hospitality extended by Professor Zhongqi Quentin Yue, Honorary Professor Chack Fan Lee, and Dr Victor Li is deeply appreciated. The author also thanked Dr Victor Li for sharing the article: “Excerpts from interview with Professor Peter Lumb”, Hong Kong Statistical Society Newsletter, Vol. 9, Issue 1, 1986. This paper was drafted during the author’s sabbatical at the Institute for Risk and Reliability, Leibniz University, which was funded by the Alexander von Humboldt Foundation. Last but not least, the author is deeply indebted to Prof Jianye Ching, National Taiwan University, for sharing his many deep insights and research in Bayesian learning and for preparing all the figures, to Dr Chong Tang for his extensive editorial assistance, and to the following colleagues for their invaluable comments: Zijun Cao, Marco D'Ignazio (for updating CLAY/9/249), Sina Javankhoshdel, C. Hsein Juang, Leena Korkiala-Tanttu, Tim Länsivaara, Stefan Larsson (for updating SE-CLAY/4/499), Andy Yat-fai Leung, Monica Löfman, Sukumar Pathmanandavel, Anders Prästings, Mengfen Shen (for updating liquefaction databases), Yu Wang (for discussions on Bayesian compressive sampling), and Dongming Zhang (for updating SH-CLAY/11/4051).

Disclosure statement

No potential conflict of interest was reported by the author.

ORCID

Kok-Kwang Phoon http://orcid.org/0000-0003-2577-8639