报告摘要
In this talk, we will present our recent progress on the micro-macro-mechanical investigation of granular soils subject to triaxial shearing using an integrated approach of X-ray micro computed tomography, three-dimensional discrete element modelling and deep learning。
A special focus will be placed on the recent development of data-driven constitutive models of granular soils. Our results show that the effects of particle morphology, confining pressure, and initial sample density on the constitutive responses of real granular soils can be well captured by the typical recurrent neural network models such as long short-term memory neural network (LSTM) and gate recurrent unit neural networks (GRU)。
The developed deep learning model can learn and reflect the intrinsic physical mechanisms underlying the granular material behaviour such as stress-strain, volumetric compression and dilatancy, strain hardening and softening, and shear-induced fabric evolutions very well. This study opens a new avenue towards the micromechanics-based constitutive modelling of granular materials。
主讲人简介
Prof. Wang is an internationally renowned expert in the field of micromechanical characterization and modeling of granular soils. Prof. Wang has received a number of prestigious international research awards including 2022 VEBLEO fellow, IAAM award, 2011 Geotechnical Research Medal (UK Institution of Civil Engineers) and 2010 Higher Education Institutions Outstanding Research Award (the Ministry of Education of China)。
Dr. Wang currently serves as an Associate Editor of Géotechnique, and Editorial Board Member of Computers and Geotechnics, Journal of Rock Mechanics and Geotechnical Engineering, and Soils and Foundations. So far Dr. Wang has published over 100 SCI journal papers and has a Google Scholar H-index of 33. Dr. Wang is among the list of world's top 2% scientists in 2022。
协办单位：浙江省力学学会岩土力学与工程专业委员会、浙江省岩土力学与工程学会本构理论与数值分析专业委员会、浙江大学岩土工程计算中心