Dr. Ning ZHANG

E-mail : ning-cee.zhang@polyu.edu.hk


Academic qualifications

  • Ph.D. in Civil Engineering, Shanghai Jiao Tong University, China, 2013 – 2019
  • B.Sc. in Civil Engineering, Southeast University, China, 2009 – 2013

Professional experience (University)

  • Research Assistant, The Hong Kong Polytechnic University, Hong Kong, China, 03/2022 - present
  • Postdoc fellow, Shantou University, China, 03/2020 - 03/2022

Research interests

  • Improvement on deep learning method in geotechnical engineering
  • Smart construction of shield tunnelling
  • Deep learning-based constitutive modeling of soils

Topic 1:Improvement on deep learning methods applied in geotechnical engineering

  • Contribution: Novel cost function and activation function for deep learning method
  • Significance: Improving the accuracy and convergence rate of deep learning methods in geotechnical engineering



  • Shen, S. L., Zhang, N.,* Zhou, A., & Yin, Z. Y. (2022). Enhancement of neural networks with an alternative activation function tanhLU. Expert Systems with Applications, 199, 117181.
  • Zhang, N., Shen, S.L.*, Zhou, A., Xu, Y.S. (2019). Investigation on performance of neural networks using quadratic relative error cost function. IEEE Access, 7, 106642-106652.


Topic 2:Smart construction of shield tunnelling

  • Contribution: Proposed intelligent models to predict and optimize the operation of tunnelling.
  • Significance: Guide for tunnelling construction with high-efficiency.



  • Zhang, N., Zhou, A., Pan Y., Shen, S.L.*, (2021). Measurement and prediction of tunnelling-induced ground settlement in karst region by using expanding deep learning method. Measurement 183, 109700.
  • Gao, M.Y., Zhang, N.*, Shen, S.L.*, Zhou, A. (2020). Real-time dynamic earth-pressure regulation model for shield tunneling by integrating GRU deep learning method with GA optimization. IEEE Access, 8, 64310-64323.
  • Zhang, N., Shen, S.L.*, Zhou, A., Arulrajah, A. (2018). Tunneling induced geohazards in mylonitic rock faults with rich groundwater: A case study in Guangzhou. Tunnelling and Underground Space Technology, 74, 262-272.


Topic 3:Stress-strain modelling of soil based on deep learning methods

  • Contribution: A new trial to reproduce soil stress–strain behaviour by adapting a long short-term memory (LSTM) deep learning method.
  • Significance: Pointing out a common issue called “bias at low stress levels”in deep learning-based constitutive models.



  • Zhang, N., Shen, S.L.*, Zhou, A., Jin, Y.F. (2021). Application of LSTM approach for modelling stress–strain behaviour of soil. Applied Soft Computing, 100, 106959.