Keynote Lecture
| Keynote Lecture 5 | Bayesian Perspective on Ground Property Variability for Geotechnical Practice |
| Date / Time | 12 December 2019, Thursday / 14:10 - 14:40 hrs |
| Venue | Room IB-101 |
| Speaker | Yu Wang City University of Hong Kong, Hong Kong |
Biography
Dr. Yu Wang , is an Associate Professor of geotechnical engineering at City University of Hong Kong. He obtained his PhD degree from Cornell University. He is a Registered Professional Engineer in Hong Kong, a Fellow of ASCE, and a past president of ASCE Hong Kong Section. His recent research efforts have been focused on analytics and simulation of spatially varying but sparsely measured geo-data, geotechnical uncertainty, reliability and risk, development and application of Bayesian methods in geotechnical engineering, and machine learning in geotechnical engineering. His research has earned several international recognitions, including the Highly Cited Research Award by the international journal of Engineering Geology in 2017, the First-class Award of the Natural Science Award in Hubei Province in 2017, the GEOSNet Young Researcher Award by the Geotechnical Safety Network (GEOSNet) in 2015 in the Netherlands, and the Wilson Tang Best Paper Award in 2012 in Singapore. He has authored/co-authored more than 150 technical publications, including 2 books and over 90 journal papers.
Abstract
Soils and rocks are natural heterogeneous geo-materials, and their properties exhibit site-specific spatial variability as an outcome of the previous geological processes that the soils and rocks in the site have undergone. Spatial variability of ground properties and other geotechnical uncertainties may be modelled probabilistically using random variables or random field. Some questions are frequently raised by practicing geotechnical engineers when they consider using probabilistic methods. For example, what is the physical meaning of failure probability and random variable or random field modeling? Is a large amount of data necessary for using probabilistic methods? This paper aims at providing answers to these questions from a Bayesian perspective. Bayesian methods and tools are also presented that were recently developed for characterization of ground property variability from sparse site investigation data.
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