Numerical Analysis of Stability and Risk in Highly Variable Soils
时 间：2018年1月4日（周四） 13:30—15:30
Vaughan Griffiths is a Professor of Civil Engineering at the Colorado School of Mines. He has written over 300 research papers, including some of the most highly cited in the geotechnical engineering research literature. He is the co-author of three textbooks on finite elements, risk assessment and numerical methods that have gone into multiple editions including the Chinese language. He gives regular short-courses for practitioners on risk and finite element applications in geotechnical engineering both in the US and overseas, with courses recently given in USA, Canada, Australia, Colombia and Norway. In 2018, further courses will be given in New Zealand. Dr. Griffiths is a former ASCE Director and recently Chaired the ASCE/G-I Specialty conference Geo-Risk 2017 in Denver. He is currently an Editor of Computers and Geotechnics, on the Advisory Panel of Géotechnique and was formerly an editor of the ASCE J Geotech Geoenv. He was the recent recipient of the 2017 H. Bolton Seed Medal at the Geotechnical Frontiers conference in Orlando and was nominated for the Cross-Canada Lecture Tour in the Spring of 2017 by the Canadian Geotechnical Society.
There has been a rapid growth of interest in risk assessment, and the use of probabilistic methods in geotechnical engineering. This is a logical development, since soils and rocks are among the most variable of all engineering materials, and geotechnical engineers must often make do with materials they are dealt with at any particular site. Analysis of a typical stability problem may lead to a “probability of failure”, as opposed to the more traditional “factor of safety”, representing a fundamental shift in the way engineers need to think about the suitability of their designs. The lecture will include results of stability analyses of variable soils by the finite element method, and include a discussion of the factor of safety and its relationship to the probability of failure. Some methods of probabilistic analysis with varying levels of complexity will be described.
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