Ning ZHANG

Ning ZHANG

Research Assistant Professor

Publications

Dr. Zhang has published over 45 articles in top-tier international journals, with more than 1700 citations on Google Scholar and an h-index of 23. You can also find his articles on the Google Scholar profile.
Numbered and Justified Text
Under review
  1. Liu, H.C., Zhang, N.*, Yin, Z-Y. (2025). Kriging-informed graph stochastic neural network for probabilistic subsurface stratification considering spatial variability. Geotechnique, Under review
  2. Liu, H.C., Zhang, N.*, Yin, Z-Y., Ruan, Z.J., Li, K.Q. (2024). Probabilistic characterization of CPTu data using kriging-informed graph convolutional neural network. Journal of Geotechnical and Geoenvironmental Engineering, Under review
First-authored or Corresponding-authored papers
  1. Xu, H.R., Zhang, N.*, Yin, Z-Y., Atangana Njock, P.G. (2025). Multimodal intelligent geotechnical design framework integrating multiple large language models. Automation in Construction, 176, 106257. https://doi.org/10.1016/j.autcon.2025.106257
  2. Zhang, H., Yin, Z-Y., Zhang, N.*, Wang, X. (2025). Self-supervised Transformer for 3D point clouds completion and morphology evaluation of granular particle. Applied Soft Computing, 176, 113161. https://doi.org/10.1016/j.asoc.2025.113161
  3. Zhang, N., Xu, K.P., Yin, Z-Y., Li, K.Q. (2025). Transfer Learning-Enhanced Finite Element-Integrated Neural Networks. International Journal of Mechanical Sciences, 290, 110075. https://doi.org/10.1016/j.ijmecsci.2025.110075
  4. Xu, H.R., Yin, J.N., Zhang, N.* (2025). Transformer-based deformation measurement of underground structure from a single-camera video. Automation in Construction, 172, 106070. https://doi.org/10.1016/j.autcon.2025.106070
  5. Liu, H.C., Zhang, N.*, Yin, Z-Y. (2025). Probabilistic stratigraphic modeling from sparse boreholes based on deep learning. Geotechnique, Ahead of Print. https://doi.org/10.1680/jgeot.24.00998
  6. Xu, K.P., Zhang, N.*, Yin, Z-Y., Li, K.Q. (2025). Finite element-integrated neural network for inverse analysis of elastic and elastoplastic boundary value problems. Computer Methods in Applied Mechanics and Engineering, 436, 117695. https://doi.org/10.1016/j.cma.2024.117695
  7. Liu, H.C., Zhang, N.*, Yin, Z-Y., Wang, Y. (2025). Interpretation of subsurface stratigraphic variations from limited boreholes using Dual Bi-LSTM. Canadian Geotechnical Journal, 62, 1–15. https://doi.org/10.1139/cgj-2024-0455
  8. Zhang, N., Xu, K.P., Yin, Z-Y., Jin, Y. F., Li, K.Q. (2025). Finite element-integrated neural network framework for elastic and elastoplastic solids. Computer Methods in Applied Mechanics and Engineering, 433, 117474. https://doi.org/10.1016/j.cma.2024.117474
  9. Xu, H.R., Zhang, N.*, Yin, Z-Y., Atangana Njock, P.G. (2025). GeoLLM: A Specialized Large Language Model Framework for Intelligent Geotechnical Design. Computers and Geotechnics, 77, 106849. https://doi.org/10.1016/j.compgeo.2024.106849
  10. Zhang, H., Yin, Z-Y., Zhang, N.*, Wang, X. (2024). A rapid segmentation and occlusion completion method for packed granular particles considering uncertainty. Canadian Geotechnical Journal, 62, 1-18. https://doi.org/10.1139/cgj-2023-0756
  11. Qiu, Y.S., Yin, J.N., Zhang, N.*, Liu, H.C., Xu, C.J. (2024). Novel graph convolutional network for geological profile prediction using non-equidistant borehole data. Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, 1–15. https://doi.org/10.1080/17499518.2024.2422489
  12. Zhang, H., Yin, Z-Y., Zhang, N.*, Wang, X. Ding, Z. (2024). A scale-adaptive Mask R-CNN strategy for foreground particle segmentation and geometrical analysis of granular aggregates. Applied Soft Computing, 111931. https://doi.org/10.1016/j.asoc.2024.111931
  13. Qiu, Y.S., Zhang, N.*, Yin, Z-Y, Wang, Y., Xu, C.J., Zhang, P. (2024). Novel multi-spatial receptive field (MSRF) XGBoost method for predicting geological cross-section based on sparse borehole data. Engineering Geology, 107604. https://doi.org/10.1016/j.enggeo.2024.107604
  14. Kong, H. Q., Zhang, N.* (2024). Risk assessment of water inrush accident during tunnel construction based on FAHP-I-TOPSIS. Journal of Cleaner Production, 141744. https://doi.org/10.1016/j.jclepro.2024.141744
  15. Zhang, N., Zhou, A., Jin, Y. F., Yin, Z-Y., Shen, S. L. (2023). An enhanced deep learning method for accurate and robust modelling of soil stress-strain response, Acta Geotechnica, 18(8), 4405-4427. https://doi.org/10.1007/s11440-023-01813-8
  16. 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. https://doi.org/10.1016/j.eswa.2022.117181
  17. Zhang, N., Zhang, N.*, Zheng, Q., Xu, Y. S. (2022). Real-time prediction of shield moving trajectory during tunnelling using GRU deep neural network. Acta Geotechnica, 17(4), 1167-1182. https://doi.org/10.1007/s11440-021-01319-1
  18. Lin, S. S., Zhang, N.*, Zhou, A., Shen, S. L. (2022). Time-series prediction of shield movement performance during tunneling based on hybrid model. Tunnelling and Underground Space Technology, 119, 104245. https://doi.org/10.1016/j.tust.2021.104245
  19. 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. https://doi.org/10.1016/j.measurement.2021.109700
  20. 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. https://doi.org/10.1016/j.asoc.2020.106959
  21. Lu, S. L., Zhang, N.*, Shen, S. L., Zhou, A., Li, H. Z. (2020). A deep-learning method for evaluating shaft resistance of the cast-in-site pile on reclaimed ground using field data. Journal of Zhejiang University-Science A, 21(6), 496-508. https://doi.org/10.1631/jzus.A1900544
  22. 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. https://doi.org/10.1109/ACCESS.2020.2984515
  23. Atangana Njock, P. G., Zheng, Q., Zhang, N.*, Xu, Y. S. (2020). Perspective review on subsea jet trenching technology and modeling. Journal of Marine Science and Engineering, 8(6), 460. https://doi.org/10.3390/jmse8060460
  24. Lin, S. S., Zhang, N.*, Xu, Y. S., Hino, T. (2020). Lesson learned from catastrophic floods in Western Japan in 2018: Sustainable perspective analysis. Water, 12(9), 2489. https://doi.org/10.3390/w12092489
  25. Zhang, N., Shen, J. S., Lin, C., Arulrajah, A., Chai, J. C. (2019). Investigation of a large ground collapse and countermeasures during mountain tunnelling in Hangzhou: a case study. Bulletin of Engineering Geology and the Environment, 78(2), 991-1003. https://doi.org/10.1007/s10064-017-1098-0
  26. 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. https://doi.org/10.1109/ACCESS.2019.2930520
  27. Zhang, N., Shen, J. S., 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. https://doi.org/10.1016/j.tust.2017.12.021
  28. Zhang, N., Wu, H. N., Shen, J. S. L., Hino, T., Yin, Z-Y. (2017). Evaluation of the uplift behavior of plate anchor in structured marine clay. Marine Georesources Geotechnology, 35(6), 758-768. https://doi.org/10.1080/1064119X.2016.1240273
  29. Ren, D. J., Shen, S. L., Cheng, W. C., Zhang, N.*, Wang, Z. F. (2016). Geological formation and geo-hazards during subway construction in Guangzhou. Environmental Earth Sciences, 75(11), 1-14. https://doi.org/10.1007/s12665-016-5710-6
  30. Zhang, N., Shen, S. L., Wu, H. N., Chai, J. C., Xu, Y. S., Yin, Z-Y. (2015). Evaluation of effect of basal geotextile reinforcement under embankment loading on soft marine deposits. Geotextiles and Geomembranes, 43(6), 506-514. https://doi.org/10.1016/j.geotexmem.2015.05.005
Co-authored papers
  1. Atangana Njock, P. G., Yin, Z-Y., Xu, H. R., Zhang, N. (2025). Structural Failure Risk Assessment of Shield Tunnel using Large Language Model. Tunnelling and Underground Space Technology, Accept
  2. Li, K. Q., Yin, Z-Y., Zhang, N., Liu, H. C. (2025). Physics-informed neural networks for solving steady-state temperature field in artificial ground freezing. Canadian Geotechnical Journal, 62, 1–17. https://doi.org/10.1139/cgj-2024-0650
  3. Jin, Z. H., Zhang, W, Yin, Z-Y., Zhang, N., Geng, X. Y. (2025). Estimating track geometry irregularities from in-service train accelerations using deep learning. Automation in Construction, 173, 106114. https://doi.org/10.1016/j.autcon.2025.106114
  4. Atangana Njock, P. G., Yin, Z-Y., Zhang, N. (2025). High-fidelity data augmentation for few-shot learning in jet grout injection applications. International Journal for Numerical and Analytical Methods in Geomechanics, 49(1), 83-100. https://doi.org/10.1002/nag.3862
  5. Li, K. Q., Yin, Z-Y., Zhang, N., &Li, J. (2024). A PINN-based modelling approach for hydromechanical behaviour of unsaturated expansive soils. Computers and Geotechnics, 169, 106174. https://doi.org/10.1016/j.compgeo.2024.106174
  6. Li, K. Q., Yin, Z-Y., Zhang, N., Liu, Y. (2023). A data-driven method to model stress-strain behaviour of frozen soil considering uncertainty. Cold Regions Science and Technology, 103906. https://doi.org/10.1016/j.coldregions.2023.103906
  7. Atangana Njock, P. G., Zhang, N., Zhou, A., Shen, S. L. (2023). Evaluation of lateral displacement induced by jet grouting using improved random forest. Geotechnical and Geological Engineering, 41(1), 459-475. https://doi.org/10.1007/s10706-022-02270-y
  8. Zhang, J. X., Zhang, N., Zhou, A., Shen, S. L. (2022). Numerical evaluation of segmental tunnel lining with voids in outside backfill. Underground Space, 7(5), 786-797. https://doi.org/10.1016/j.undsp.2021.12.007
  9. Lin, S. S., Shen, S. L., Zhang, N., Zhou, A. (2022). An extended TODIM-based model for evaluating risks of excavation system. Acta Geotechnica, 17(4), 1053-1069. https://doi.org/10.1007/s11440-021-01294-7
  10. Kong, H. Q., Zhao, L. S., Zhang, N. (2022). Water inrush hazard in Shijingshan tunnel during construction, Zhuhai, Guangdong, China. Safety, 8(1), 7. https://doi.org/10.3390/safety8010007
  11. Lin, S. S., Zhang, N., Zhou, A., Shen, S. L. (2022). Risk evaluation of excavation based on fuzzy decision-making model. Automation in Construction, 136, 104143. https://doi.org/10.1016/j.autcon.2022.104143
  12. Lin, S. S., Shen, S. L., Zhou, A., Zhang, N. (2021). Ensemble model for risk status evaluation of excavation. Automation in Construction, 132, 103943. https://doi.org/10.1016/j.autcon.2021.103943
  13. Lin, S. S., Shen, S. L., Zhang, N., Zhou, A. (2021). Modelling the performance of EPB shield tunnelling using machine and deep learning algorithms. Geoscience Frontiers, 12(5), 101177. https://doi.org/10.1016/j.gsf.2021.101177
  14. Lin, S. S., Shen, S. L., Zhang, N., Zhou, A. (2021). Comprehensive environmental impact evaluation for concrete mixing station (CMS) based on improved TOPSIS method. Sustainable Cities and Society, 69, 102838. https://doi.org/10.1016/j.scs.2021.102838
  15. Xu, Y. S., Wu, H. N., Shen, J. S., Zhang, N. (2017). Risk and impacts on the environment of free-phase biogas in Quaternary deposits along the coastal region of Shanghai. Ocean Engineering, 137, 129-137. https://doi.org/10.1016/j.oceaneng.2017.03.051