Dr. Yin-Fu JIN

E-mail : yinfu.jin@polyu.edu.hk


Academic qualifications

  • Ph.D. in Geotechnical Engineering, Ecole Centrale de Nantes, France, 2013 – 2016

Professional experience (University)

  • Postdoc fellow, The Hong Kong Polytechnic University, Hong Kong, China, 12/2018 – present
  • Postdoc fellow, University of Macau, Macau, China, 03/2017-03/2018

Research interests

  • Artificial intelligence in geotechnical engineering
  • Finite element large deformation analysis for geotechnical engineering
  • Constitutive modeling of soils

Topic 1:Enhancement of Optimization theory and Bayesian method and software platform development

  • Contribution: Proposed efficient and fast advanced optimization algorithms and improved Bayesian methods for parameter identification, and developed a practical software platform ErosOpt.
  • Significance: Improving the speed and accuracy of parameter identification, and facilitating the use of engineering practitioners.


  • Jin Y-F, Yin Z-Y. ErosLab: a modelling tool for soil tests. Advances in Engineering Software 2018; 121(84-97).
  • Jin Y-F, Yin Z-Y. Enhancement of backtracking search algorithm for identifying soil parameters. International Journal for Numerical and Analytical Methods in Geomechanics 2020; 44(9): 1239-1261.
  • Jin Y-F, Yin Z-Y, Zhou W-H, Huang H-W. Multi-objective optimization-based updating of predictions during excavation. Engineering Applications of Artificial Intelligence 2019; 78(102-123.
  • Jin Y-F, Yin Z-Y, Zhou W-H, Shao J-F. Bayesian model selection for sand with generalization ability evaluation. International Journal for Numerical and Analytical Methods in Geomechanics 2019; 43(14): 2305-2327.
  • Yin Z-Y, Jin Y-F, Shen JS, Hicher P-Y. Optimization techniques for identifying soil parameters in geotechnical engineering: Comparative study and enhancement. International Journal for Numerical and Analytical Methods in Geomechanics 2018; 42(1): 70-94.
  • Yin Z-Y, Jin Y-F, Shen S-L, Huang H-W. An efficient optimization method for identifying parameters of soft structured clay by an enhanced genetic algorithm and elastic–viscoplastic model. Acta Geotechnica 2017; 12(4): 849-867.


Topic 2:Enhancement of Optimization theory and Bayesian method and software platform development

  • Contribution: The intelligent identification and model selection process based on optimization/Bayesian advanced constitutive model parameters are proposed, and the evolutionary polynomial regression (EPR) prediction model of soil mechanical parameters is proposed.
  • Significance: The model parameters of the advanced constitutive model can be quickly obtained, which facilitates the use of advanced constitutive models in actual projects, and reduces the threshold for using advanced constitutive models.



  • Application to excavation: model selection and updating



  • Model selection based on field data (7 clay models)


  • Jin Y-F, Wu Z-X, Yin Z-Y, Shen JS. Estimation of critical state-related formula in advanced constitutive modeling of granular material. Acta Geotechnica 2017; 12(6): 1329-1351.
  • Jin Y-F, Yin Z-Y. An intelligent multi-objective EPR technique with multi-step model selection for correlations of soil properties. Acta Geotechnica 2020; 15(8): 2053-2073.
  • Jin Y-F, Yin Z-Y, Shen S-L, Hicher P-Y. Investigation into MOGA for identifying parameters of a critical-state-based sand model and parameters correlation by factor analysis. Acta Geotechnica 2016; 11(5): 1131-1145.
  • Jin Y-F, Yin Z-Y, Shen S-L, Hicher P-Y. Selection of sand models and identification of parameters using an enhanced genetic algorithm. International Journal for Numerical and Analytical Methods in Geomechanics 2016; 40(8): 1219-1240.
  • Jin Y-F, Yin Z-Y, Shen S-L, Zhang D-M. A new hybrid real-coded genetic algorithm and its application to parameters identification of soils. Inverse Problems in Science and Engineering 2017; 25(9): 1343-1366.
  • Jin Y-F, Yin Z-Y, Zhou W-H, Horpibulsuk S. Identifying parameters of advanced soil models using an enhanced transitional Markov chain Monte Carlo method. Acta Geotechnica 2019; 14(6): 1925-1947
  • Jin Y-F, Yin Z-Y, Zhou W-H, Liu X. Intelligent model selection with updating parameters during staged excavation using optimization method. Acta Geotechnica 2020; 15(9): 2473-2491.
  • Jin Y-F, Yin Z-Y, Zhou W-H, Yin J-H, Shao J-F. A single-objective EPR based model for creep index of soft clays considering L2 regularization. Engineering Geology 2019; 248(242-255).
  • Yin Z-Y, Jin Y-F, Huang H-W, Shen S-L. Evolutionary polynomial regression based modelling of clay compressibility using an enhanced hybrid real-coded genetic algorithm. Engineering Geology 2016; 210(158-167).


Topic 3:Modelling of geotechnical large deformation problems

  • Contribution: Developed a edge-based strain smoothed particle finite element program (SPFEM) for large deformation calculations
  • Significance: It can simulate large deformations of common geotechnical engineering, predict the scale of disasters, and assess risks.



  • Jin Y-F, Yin Z-Y, Wu Z-X, Daouadji A. Numerical modeling of pile penetration in silica sands considering the effect of grain breakage. Finite Elements in Analysis and Design 2018; 144(15-29).
  • Jin Y-F, Yin Z-Y, Wu Z-X, Zhou W-H. Identifying parameters of easily crushable sand and application to offshore pile driving. Ocean Engineering 2018; 154(416-429).
  • Jin Y-F, Yin Z-Y, Yuan W-H. Simulating retrogressive slope failure using two different smoothed particle finite element methods: A comparative study. Engineering Geology 2020; 279(105870).
  • Jin Y-F, Yuan W-H, Yin Z-Y, Cheng Y-M. An edge-based strain smoothing particle finite element method for large deformation problems in geotechnical engineering. International Journal for Numerical and Analytical Methods in Geomechanics 2020; 44(7): 923-941.