Dr. Kai-Qi LI
G o o g l e Scholar | Research Gate

Academic qualifications

  • Ph.D. in Hydraulic Structural Engineering, Wuhan University, 2017 – 2022

Research interests

  • Multi-scale and multi-field coupling theory
  • Random finite element and uncertainty quantification
  • Microstructure characterization and reconstruction
  • Ground freezing technique
  • Artificial intelligence in geotechnical engineering

Topic 1:Uncertainty quantification of geotechnical parameters based on multi-scale theory

  • Contribution: proposed a numerical model to predict the geomaterials properties based on internal structure, explored the scale effect of the anisotropy in geomaterials properties, and developed a simple upscaling strategy for application.
  • Significance: shedding light on the relationship between micro/meso-structure of geomaterial and macro properties, providing an efficient and practical tool for predicting the geomaterials (an)isotropic properties.


  • Li, K. Q., Li, D. Q., & Liu, Y. (2020). Meso-scale investigations on the effective thermal conductivity of multi-phase materials using the finite element method. International Journal of Heat and Mass Transfer, 151, 119383.
  • Li, K. Q., Li, D. Q., Chen, D.H., Gu, S.X., & Liu, Y. (2021). A generalized model for effective thermal conductivity of soils considering porosity and mineral composition. Acta Geotechnica, 16, 3455-3466.
  • Li, K. Q., Miao, Z., Li, DQ., & Liu, Y. (2022). Effect of mesoscale internal structure on effective thermal conductivity of anisotropic geomaterials. Acta Geotechnica, 17,3553–3566.


Topic 2:Estimating soil properties using machine learning algorithms (MLA)

  • Contribution: compiled a systematic database of soil thermal properties and proposed a generalized model to predict the thermal conductivity of soil (k).
  • Significance: selecting an excellent MLA for the assessment of k, providing a reference for evaluating k via MLA.



  • Li, K. Q., Liu, Y., & Kang, Q. (2022). Estimating the thermal conductivity of soils using six machine learning algorithms. International Communications in Heat and Mass Transfer, 136, 106139.
  • Li, K. Q., Kang, Q., Nie, J. Y., & Huang, X. W. (2022). Artificial neural network for predicting the thermal conductivity of soils based on a systematic database. Geothermics, 103, 102416.


Topic 3:Investigation of impacts of uncertainties on artificial ground freezing technique (AGF)

  • Contribution: proposed a coupled thermo-hydraulic finite element method to examine the effects of uncertainties on AGF.
  • Significance: offering practitioners a rule of thumb for estimating freeze pipe spacing, reducing construction risks and saving cost.



  • Liu, Y., Li, K. Q., Li, D. Q., Tang, X. S., & Gu, S. X. (2022). Coupled thermal–hydraulic modeling of artificial ground freezing with uncertainties in pipe inclination and thermal conductivity. Acta Geotechnica, 17(1), 257-274.
  • Liu, Y., Li, K. Q., Li, P. T., & Hu, J. (2019). Artificial ground freezing technique in tunnel construction considering uncertain drilling inaccuracy of freeze pipes. In Proceedings of the 29th European safety and reliability conference, Hannover, Germany.