Zhen Liu

Zhen Liu

Contact

  • Assistant Professor, Civil and Environmental Engineering
  • Affiliated Assistant Professor, Geological and Mining Engineering and Sciences
  • PhD, Case Western Reserve University, Civil Engineering
  • MS, Zhejiang University, Civil Engineering-Geotechnical Engineering
  • BS, Lanzhou Jiaotong University, Civil Engineering-Bridge Engineering

Biography

Dr. Liu earned his Ph.D. in Civil Engineering, with an emphasis in Geotechnical Engineering, from Case Western Reserve University in Cleveland, Ohio, in 2012. He continued working at Case as a research associate before joining the department. His teaching interests include soil mechanics, foundation engineering, numerical simulations, and other topics in classical mechanics. His research interests are integrated as the multiphysics simulation and innovative characterization in porous materials. The scope covers the numerical simulation and experimental measurement of multiphysical phenomena such as freezing, hydration, and dissociation and covers porous materials such soils, cement-base materials, gas hydrates, and biomaterials. His research has many direct applications in infrastructure sustainability, energy resources, environment protection, and advanced materials. Dr. Liu is currently serving as an active member on the ASCE task committee (Engineering Geology) and TRB standing committees (Physicochemical and Biological Processes, Climatic Effects, Unsaturated Soils).

Links of Interest

Teaching Interests

  • Soil mechanics and labs
  • Foundation engineering
  • Numerical simulations (FEM, FDM)
  • Multiphysics in Porous Materials

Research Interests

  • Multiphysical phenomena: soil freezing, cement hydration, gas hydrate dissociation, energy geotechnics, electrokinetics, electromagnetics in soils, and soil-structure-fluid interaction
  • Advanced soil mechanics: stress formulation, soil wetting, adsorption, non-isothermal soil mechanics, phase change, and soil behavior under extreme conditions
  • Geo-sensing with innovative sensor techniques (acoustic, electromagnetic) and geo-intelligence with machine learning (intelligent geo-systems, data-driven geotechnics)
  • Numerical simulation techniques: multiphysics, molecular dynamics, multiscale-driven multiphysics, XFEM, SPH