Keynote Lecture
| Keynote Lecture 2 | Quantitative Risk Assessment of Individual Landslides |
| Date / Time | 13 December 2019, Friday / 14:10 - 14:40 hrs |
| Venue | Room IB-101 |
| Speaker | Jinsong Huang University of Newcastle, Australia |
Biography
Jinsong Huang obtained his BE, MS and PhD Degrees in Civil Engineering from Huazhong University of Science and Technology, China. He started his academic career at China's Wuhan University in 1997 and was promoted to Associate Professor in 1999. From 2005-2010, he was employed as a Research Associate Professor at the Colorado School of Mines, USA. He joined the ARC Centre of Excellence for Geotechnical Science and Engineering node at the University of Newcastle as a Research Academic in 2010. He is now a full professor. His research interests include risk assessment in geotechnical engineering and computational geomechanics. He has published over 100 journal papers on the risk assessment of slope stability and landslides, the modelling of spatial variability, stress integration techniques for elastoplastic models, the contact dynamics of granular media, and the analysis of hydraulic fracturing. He received a Regional Contribution Award from the International Association of Computer Methods and Advances in Geomechanics at its international conference in Kyoto in 2014 and the GEOSNet Award from the Geotechnical Safety Network in 2017. He served as the editor-in-chief of the special issue “Modelling spatial variability in geotechnical engineering” in Georisk, and he is an editorial board member for this journal as well as Canadian Geotechnical Journal and Computers and Geotechnics. He has been invited to deliver several lectures at international conferences, universities and industry, and is a committee member on the ASCE Geo-Institute's Technical Committee on Risk Assessment and Management (RAM) and the ISSMGE Technical Committee (TC304) on Engineering Practice of Risk Assessment & Management. He serves as chair of technical program committee of the upcoming Geo-Risk 2017 Geo-Institute specialty conference to be held in Denver, Colorado on June 4-7 2017.
Abstract
Slope failures or landslides are reported every year in different countries. In spite of improvements in landslide hazard recognition, prediction, mitigation measures, and warning systems, worldwide landslide activity is increasing. Uncertainty is a dominant feature of all landslides. Various uncertainties arise during the resolution of the problem, from climate data of rainfall, to infiltration rate, to site characterization, to material properties, to analysis, design and consequence assessment. These variabilities are rarely taken into account directly in traditional geotechnical analysis, rather some “average” or suitably “pessimistic” property is assumed to act across the whole region of interest. This keynote will focus on the modelling of spatial variability of soil properties in the quantitative risk assessment of individual landslides. Firstly, infinite (1D), two dimensional (2D) and three dimensional (3D) slope examples are used to demonstrate the importance of modelling spatial variability in the risk assessment of landslides. The infinite slope example shows that ignoring spatial variability will lead to unconservative estimation of slope failure. The 2D slope example shows that the slope failure can have multiple possible failure modes. The 3D slope example shows that ignoring the spatial variability in the third direction can lead to unconservative estimation of slope failure probability if the slope is long. Then the framework of quantitative risk assessment of landslides is discussed. Slope or landslides can fail shallowly or deeply. Because deep failure leads to more severe consequence than shallow failure, each potential failure mode has a specific consequence associated with it. It is thus necessary to redefine risk as mean consequence rather than assuming a constant consequence for all failure modes. One challenge for quantitative risk assessment of landslides is to automatically identify failure modes and assess associated consequences. This issue can be overcome by introducing Machine Learning algorithms within the framework of Monte Carlo simulations.
Full Text (PDF)