Pin ZHANG

G o o g l e Scholar | Research Gate

E-mail : pin-cee.zhang@connect.polyu.hk


Academic qualifications

  • Ph.D. Candidate in Geotechnical Engineering, Hong Kong Polytechnic University, 2020 - now

Research interests

  • Application of machine learning in soil mechanics and geotechnical engineering
  • Image-based particle reconstruction and morphology identification
  • In-situ investigation of soil-structure interaction (tunnel, pile, etc.)

Topic 1：Machine learning based modelling of mechanical properties and behaviours of soils

  • Contribution: developed machine learning based models to predict soil mechanical properties such as compression and creep index, and path-dependent behaviours.
  • Significance: improving the prediction accuracy of soil properties, simplifying the process of developing a soil constitutive model.



  • Zhang P, Yin ZY, Jin YF, Chan THT, 2020. A Novel Hybrid Surrogate Intelligent Model for Creep Index Prediction based on Particle Swarm Optimization and Random Forest. Engineering Geology. 265, 105328 (ESI).
  • Zhang P, Yin ZY, Jin YF, 2021. State-of-the-Art Review of Machine Learning Applications in Constitutive Modeling of Soils. Archives of Computational Methods in Engineering, https://doi.org/10.1007/s11831-020-09524-z.
  • Zhang P, Yin ZY, Jin YF, Ye GL, 2020. An AI-based model for describing cyclic characteristics of granular materials. International Journal for Numerical and Analytical Methods in Geomechanics, 44, 9: 1315-1335.

Topic 2: Data-driven surrogate model with application in engineering practice

  • Contribution: developed data-driven surrogate models to capture the responses of soil-structure interaction
  • Significance: saving computational cost to implement the simulation of engineering practice


  • Zhang P, Yin ZY, Zheng YY, Gao FP, 2020. A LSTM Surrogate Modelling Approach for Caisson Foundations. Ocean Engineering, 204, 107263.
  • Chen RP, Zhang P*, Kang X, Zhong ZQ, Liu Y, Wu HN, 2019. Prediction of maximum surface settlement caused by EPB shield tunneling with ANN methods. Soils and Foundations. 59: 284–295 (ESI).
  • Zhang P, Chen RP, Wu HN, 2019. Real-time Analysis and Regulation of EPB Shield Steering Using Random Forest. Automation in Construction. 106: 101860.

Topic 3: Investigation of impacts of engineering construction on surrounding environment

  • Contribution: understanding the disturbance mechanism and range during pile driving and tunnelling
  • Significance: reducing construction risks and saving cost

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