Dr. Te Xiao (肖特) is an Associate Professor at the Department of Civil Engineering, Shanghai Jiao Tong University. His main research interests include geotechnical risk and reliability, machine learning and digital twins, uncertainty characterization in site investigation, landslide hazard chains, risk-informed decision-making, etc. He has authored 40+ peer-reviewed journal papers and one academic monograph. He is the recipient of the NSFC Excellent Young Scientists Fund (Overseas)，ISSMGE Bright Spark Lecture Award, and HKIE Geotechnical Paper Award.

🔥 News

• 2025.10: 🎉🎉 Bingkun Song makes a presentation at the Third Workshop on the Future of Machine Learning in Geotechnics (3FOMLIG), Florence, Italy.
• 2025.09: 🎉🎉 Welcome Xianchong Li, Bingkun Song, and Shakhnoza Kambaralieva (Sasha) to our research group! Sasha from Uzbekistan is the first international student in our group.

• 2025.08: 🎉🎉 Dr. Te Xiao chairs the IS8 (Risk management practice in geotechnical engineering) at the 9th International Symposium for Geotechnical Safety and Risk, Oslo, Norway, with Prof. Lulu Zhang and Dr. Jian He.

• 2025.04: 🎉🎉 Dr. Te Xiao serves as the technical secretary for Question 109 (Dams and levees fit for the future) at the 28th Congress of International Commission on Large Dams, Chengdu.

• 2025.04: 🎉🎉 Dr. Te Xiao gives an ISSMGE TC304 Short Course on Geotechnical Reliability Analysis, with Prof. Zijun Cao.

• 2024.10: 🎉🎉 Dr. Te Xiao organizes the 8th ISSMGE TC304/TC309 Student Contest on landslide detection using machine learning techniques, with Prof. Iason Papaioannou, Prof. Jinhui Li, and Dr. Ronald Schneider.

• 2024.09: 🎉🎉 Welcome Heng Zhou and Jinbo Yi to our research group.
• 2024.08: 🎉🎉 Dr. Te Xiao is awarded the NSFC Young Scientists Fund.

• 2024.05: 🎉🎉 Dr. Te Xiao wins the HKIE Geotechnical Paper Award 2024.

• 2024.01: 🎉🎉 Engineering Risk Group @SJTU is established. Welcome to following our WeChat Official Account SJU工程风险课题组.

📖 Background

Professional experiences

• 2024.01 - present, Associate Professor, Dept. of Civil Engineering, Shanghai Jiao Tong University
• 2021.07 - 2023.12, Research Assistant Professor, Dept. of Civil & Environmental Engineering, Hong Kong University of Science and Technology
• 2018.09 - 2021.06, Postdoctoral Research Fellow, Dept. of Civil & Environmental Engineering, Hong Kong University of Science and Technology
  

Educations

• 2013.09 - 2018.06, Ph.D. in Hydraulic Structure Engineering, Wuhan University
• 2009.09 - 2013.06, B.Sc. in Water Conservancy and Hydropower Engineering, Wuhan University
  
  

💼 Projects

• 2025 – 2027, PI, NSFC Excellent Young Scientists Fund (Overseas), Digital twin-powered risk mitigation and emergency management for landslide hazards
• 2025 – 2027, PI, NSFC Young Scientists Fund, Methods for regional landslide spatiotemporal prediction and risk-informed early warning using incomplete data
• 2024 – 2028, Co-I, RGC Theme-based Research Scheme, Digital twin-empowered landslide emergency risk management
• 2021 – 2024, Co-I, NSFC-RGC Joint Research Scheme, Evolution of landslide hazard chains triggered by strong earthquakes and the associated dynamic risk management
• 2023 – 2024, Co-I, Geotechnical Engineering Office of HKSAR Government, Uncertainties and target reliability of slopes
  
  

📝 Publications

Monographs

• Zhang, J., Xiao, T., Ji, J., Zeng, P., Cao, Z. (2023). Geotechnical Reliability Analysis: Theories, Methods and Algorithms. Springer, Singapore. 310p.
  

Journal papers

[Intelligent hazard risk management]

Geotechnique 2023

• Xiao, T., Zhang, L. M.*, Cheung, R. W. M., Lacasse, S. (2023). Predicting spatio-temporal man-made slope failures induced by rainfall in Hong Kong using machine learning techniques. Géotechnique, 73(9), 749-765. | [pdf] | [poster]

  Highlights

  • The machine learning model incorporates landslide time and consequences into susceptibility mapping to achieve spatio-temporal forecasting.
  • The machine learning model significantly outperforms the statistical rainfall-landslide correlations.

EG 2023

• Xiao, T., Zhang, L. M.* (2023). Data-driven landslide forecasting: methods, data completeness, and real-time warning. Engineering Geology, 317, 107068. | [pdf]

  Highlights

  • The ‘at least one failure’ strategy enables the use of incomplete data in landslide forecasting.
  • Machine learning-powered spatio-temporal prediction enables real-time landslide warning.

EG 2022

• Ju, L. Y., Xiao, T.*, He, J., Wang, H. J., Zhang, L. M. (2022). Predicting landslide runout paths using terrain matching-targeted machine learning. Engineering Geology, 311, 106902. | [pdf]

  Highlights

  • A novel machine learning model is proposed for predicting landslide runout paths.
  • Internal terrain matching is introduced to consider terrain reality and trace features.

Georisk 2025

• Qiang, Y. J., Xiao, T.*, He, J., Zhang, L. M. (2025). Multi-hazard stress testing under extreme rainstorms in the Shenzhen metropolitan area. Georisk, 19(3), 467-485. | [pdf]

  Highlights

  • The probable maximum precipitation in Shenzhen is estimated.
  • An integrated multi-hazard simulation is conducted under extreme rainstorms.

Landslides 2019

• Zhang, L. M.*, Xiao, T., He, J., Chen, C. (2019). Erosion-based analysis of breaching of Baige landslide dams on the Jinsha River, China, in 2018. Landslides, 16(10), 1965-1979. | [pdf]

  Highlights

  • A erosion-based landslide dam breaching model is proposed.
  • A case study of two Baige landslide dams on the Jinsha River is conducted.

• Ju, L. Y., Xiao, T., He, J., Xu, W. F., Xiao, S. H., Zhang, L. M.* (2025). A simulation-enabled slope digital twin for real-time assessment of rain-induced landslides. Engineering Geology, 353, 108116.
• Xiao, S. H., Xiao, T., Jiang, R. C., Wang, H. J., Ju, L. Y., Zhang, L. M.* (2024). Two-phase strategy for rapid and unbiased assessment of earthquake-induced landslides. Engineering Geology, 336, 107562.
• Wang, H. J., Xiao, T., Li, X. Y., Zhang, L. L., Zhang, L. M.* (2019). A novel physically-based model for updating landslide susceptibility. Engineering Geology, 251, 71-80.
• Xiao, S. H., Zhang, L. M.*, Xiao, T., Jiang, R. C., Peng, D. L., Lu, W. J., He, X. (2024). Landslide damming threats along the Jinsha River, China. Engineering, 42, 326-339.
• Ju, L. Y., Zhang, L. M.*, Xiao, T. (2023). Power laws for accurate determination of landslide volume based on high-resolution LiDAR data. Engineering Geology, 312, 106935.
• Qiang, Y. J., Zhang, L. M.*, Xiao, T. (2020). Spatial-temporal rain field generation for the Guangdong-Hong Kong-Macau Greater Bay Area considering climate change. Journal of Hydrology, 583, 124584.
• He, J., Zhang, L. M.*, Xiao, T., Wang, H. J., Luo, H. Y. (2023). Prompt quantitative risk assessment for rain-induced landslides. Journal of Geotechnical and Geoenvironmental Engineering, 149(5), 04023023.
• He, J., Zhang, L. M.*, Xiao, T., Wang, H. J., Luo, H. Y. (2023). Deep learning enables super-resolution hydrodynamic flooding process modelling under spatiotemporally varying rainstorms. Water Research, 239, 120057.
• Chen, C., Zhang, L. M.*, Xiao, T., He, J. (2020). Barrier lake bursting and flood routing in the Yarlung Tsangpo Grand Canyon in October 2018. Journal of Hydrology, 583, 124603.
• Qiang, Y. J., He, J., Xiao, T., Lu, W. J., Li, J. H., Zhang, L. M.* (2021). Coastal town flooding upon compound rainfall-wave overtopping-storm surge during extreme tropical cyclones in Hong Kong. Journal of Hydrology: Regional Studies, 37, 100890.
• Wang, J., Zeng, P.*, Xiao, T., Peng, M., Li, T., Zhang, H., Sun, X. (2025). Calibrating soil erodibility parameters of landslide dams using back analyses. Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, 1-22.
• Qiang, Y. J., Zhang, L. M.*, He, J., Xiao, T., Huang, H. H., Wang, H. J. (2021). Urban flood analysis for Pearl River Delta cities using an equivalent drainage method upon combined rainfall-high tide-storm surge events. Journal of Hydrology, 597, 126293.
• He, J., Zhang, L. M.*, Xiao, T., Chen, C. (2022). Emergency risk management for landslide dam breaks in 2018 on the Yangtze River, China. Resilient Cities and Structures, 1(3), 1-11.

[Geotechnical risk and reliability]

CG 2016

• Xiao, T., Li, D. Q.*, Cao, Z. J., Au, S. K., Phoon, K. K. (2016). Three-dimensional slope reliability and risk assessment using auxiliary random finite element method. Computers and Geotechnics, 79, 146-158. | [pdf]

  Highlights

  • An auxiliary random finite element method is proposed for efficient 3-D slope reliability analysis and risk assessment.
  • Spatial variability can significantly influence the failure mode, reliability and risk of 3-D slopes.

Georisk 2017

• Xiao, T., Li, D. Q.*, Cao, Z. J., Tang, X. S. (2017). Full probabilistic design of slopes in spatially variable soils using simplified reliability analysis method. Georisk, 11(1), 146-159. | [pdf]

  Highlights

  • A simplified reliability analysis method is proposed for efficient full probabilistic design of soil slopes in spatially variable soils.
  • The spatial variability has considerable effects on the optimal slope design.

Landslides 2016

• Li, D. Q., Xiao, T., Cao, Z. J.*, Zhou, C. B., Zhang, L. M. (2016). Enhancement of random finite element method in reliability analysis and risk assessment of soil slopes using Subset Simulation. Landslides, 13(2), 293-303. | [pdf]

  Highlights

  • A subset-simulation-based random finite element method (RFEM) is proposed to extend RFEM from reliability analysis to risk assessment in an efficient manner.

AMM 2016

• Li, D. Q., Xiao, T., Cao, Z. J.*, Phoon, K. K., Zhou, C. B. (2016). Efficient and consistent reliability analysis of soil slope stability using both limit equilibrium analysis and finite element analysis. Applied Mathematical Modelling, 40(9-10), 5216-5229. | [pdf]

  Highlights

  • A probabilistic simulation-based method is proposed for slope reliability analysis based on two models, i.e., a simple LEM and a complex FEM.
  • The method can obtain an efficient and unbiased estimation of failure probability.

CJGE 2025

• Xiao, T., Li, D. Q.*, Zhou, C. B. (2025). Collaborative risk assessment approach in geotechnical engineering 岩土工程协同式风险评估方法. Chinese Journal of Geotechnical Engineering, 47(7), 1335-1343. (in Chinese) | [pdf]

  Highlights

  • A collaborative risk assessment approach is proposed for real-world geotechnical engineering problems.
  • The approach can integrate the advantages of multiple models in terms of efficiency and accuracy.

• Wang, S., Xiao, T., Li, G., Lv, Y., Dai, C., Zhan, L., Chen, Y., Zhang, S.* (2024). Spatial variability characterization of clayey waste soils and its impact on probabilistic stability assessment of a landfill slope. Acta Geotechnica, 19(8), 5157-5174.
• Zhu, H., Zhang, L. M.*, Xiao, T. (2019). Evaluating the stability of anisotropically deposited soil slopes. Canadian Geotechnical Journal, 56(5), 753-760.
• Zhu, H., Zhang, L. M.*, Xiao, T., Li, X. Y. (2017). Enhancement of slope stability by vegetation considering uncertainties in root distribution. Computers and Geotechnics, 85, 84-89.
• Zhou, Z., Li, D. Q.*, Xiao, T., Cao, Z. J., Du, W. (2021). Response surface guided adaptive slope reliability analysis in spatially varying soils. Computers and Geotechnics, 132, 103966.
• Li, X. Y., Zhang, L. M.*, Xiao, T., Zhang, S., Chen, C. (2019). Learning failure modes of soil slopes using monitoring data. Probabilistic Engineering Mechanics, 56, 50-57.
• Li, X. Y., Fan, Z. B., Lu, T., Xiao, T., Zhang, L. M.* (2018). A resilience model for engineered slopes subject to anchor corrosion. KSCE Journal of Civil Engineering, 22(3), 887-895.
• Xiao, T., Li, D. Q.*, Zhou, C. B., Phoon, K. K. (2014). Non-intrusive reliability analysis of multi-layered slopes using strength reduction FEM 基于有限元强度折减法的多层边坡非侵入式可靠度分析. Journal of Basic Science and Engineering, 22(4), 718-732. (in Chinese)
• Li, D. Q., Xiao, T., Cao, Z. J.*, Tang, S. X., Phoon, K. K. (2016). Auxiliary slope reliability analysis using limit equilibrium analysis and finite element analysis 基于极限平衡法和有限元法的边坡协同式可靠度分析. Chinese Journal of Geotechnical Engineering, 38(6), 1004-1013. (in Chinese)
• Li, D. Q.*, Xiao, T., Cao, Z. J., Zhou, C. B., Phoon, K. K. (2016). Slope risk assessment using efficient random finite element method 基于高效随机有限元法的边坡风险评估. Rock and Soil Mechanics, 37(7), 1994-2003. (in Chinese)

[Probabilistic site characterization and modeling]

JGGE 2018

• Xiao, T., Li, D. Q.*, Cao, Z. J., Zhang, L. M. (2018). CPT-based probabilistic characterization of three-dimensional spatial variability using MLE. Journal of Geotechnical and Geoenvironmental Engineering, 114(5), 04018023. | [pdf]

  Highlights

  • A CPT–based probabilistic approach for characterizing 3D spatial variability underlying the framework of MLE.
  • Simultaneous vertical and horizontal characterization based on multiple CPTs is a feasible way for 3D spatial variability characterization in the presence of limited data.

JRUES 2017

• Xiao, T., Zhang, L. M.*, Li, X. Y., Li, D. Q. (2017). Probabilistic stratification modeling in geotechnical site characterization. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, 3(4), 04017019. | [pdf]

  Highlights

  • A three-level probabilistic framework is proposed for geotechnical stratification modeling considering stratigraphic uncertainty.
  • A heuristic combination model is recommended to combine the advantages of the boundary-based and category-based stratigraphic models.

BOEG 2021

• Xiao, T., Zou, H. F., Yin, K. S., Du, Y., Zhang, L. M.* (2021). Machine learning-enhanced soil classification by integrating borehole and CPTU data with noise filtering. Bulletin of Engineering Geology and the Environment, 80(12), 9157-9171. | [pdf]

  Highlights

  • A coupled machine learning method is proposed to integrate the borehole and CPTU data under a rigorous Bayesian framework and to identify and separate the noisy CPTU data.
  • The method is applied to the marine site characterization of the Hong Kong-Zhuhai-Macao Bridge.

AMM 2019

• Li, D. Q.*, Xiao, T., Zhang, L. M., Cao, Z. J. (2019). Stepwise covariance matrix decomposition for efficient simulation of multivariate large-scale three-dimensional random fields. Applied Mathematical Modelling, 68, 169-181. | [pdf] | [code]

  Highlights

  • A stepwise covariance matrix decomposition method is proposed for multivariate large-scale 3-D random field simulation.
  • The requirement on memory space can be reduced by ten orders of magnitude.

• Yin, K. S., Xiao, T., Luo, H. Y., Zou, H. F., Zhang, L. M.* (2023). Probabilistic modeling of offshore deep cement mixing improved ground. Computers and Geotechnics, 156, 105266.
• Zhu, H., Zhang, L. M.*, Xiao, T., Li, X. Y. (2017). Generation of multivariate cross-correlated geotechnical random fields. Computers and Geotechnics, 86, 95-107.
• Miao, C., Cao, Z. J.*, Xiao, T., Li, D. Q., Du, W. (2023). BayLUP: A Bayesian framework for conditional random field simulation of the liquefaction-induced settlement considering statistical uncertainty and model error. Gondwana Research, 123, 140-163.
• Wang, L., Zhang, X., Hong, Y.*, Xiao, T., Cao, Z., Li, S., Wu, S. (2025). Efficient 3D geological modeling for offshore wind farm with sparse CPTU and borehole data. Canadian Geotechnical Journal, 62, 1-23.
• Zhang, F. P., Li, D. Q., Cao, Z. J.*, Xiao, T., Zhao, J. (2018). Revisiting statistical correlation between Mohr-Coulomb shear strength parameters of Hoek-Brown rock masses. Tunnelling and Underground Space Technology, 77, 36-44.
• Li, P. P., Li, D. Q., Xiao, T.*, Cao, Z. J. (2018). Bayesian updating of excavation considering model uncertainty 考虑经验模型不确定性的基坑开挖贝叶斯更新. Journal of Natural Disasters, 27(4), 143-150. (in Chinese)

[Offshore geotechnics]

EG 2025

• Zhao, F. Y., Zhang, L. L., Xiao, T.*, Chen, Y. M. (2025). An equivalent state method for submarine spread modeling subject to hydrate dissociation. Engineering Geology, 352, 108070. | [pdf]

  Highlights

  • An equivalent state method is proposed to couple hydrate dissociation and submarine spreading.
  • Three key stages of hydrate-induced submarine spreading are identified.

Reports

• Li, D. Q., Xiao, T., Liu, X., Cao, Z. J. (2016). Introduction to NIGPA (Non-Intrusive Geotechnical Probabilistic Analysis) (Version 1.1). Wuhan University, Wuhan. 19p. | [report]
  
  

💬 Invited Talks

• 2023, Machine learning-powered landslide forecasting: from initiation to mobility, ISSMGE Bright Spark Lecture, Hong Kong | [pdf]
• 2025, Machine learning-powered landslide forecasting, Webinar Talk for The Institution of Engineers, Malaysia
• 2025, 3-D marine geological modeling driven by machine learning and data fusion, The 7th National Young Scholar Symposium on Engineering Risk Analysis & Management, Nanchang
• 2024, Machine learning for landslides: from spatio-temporal forecasting to risk assessment,The 6th National Young Scholar Symposium on Engineering Risk Analysis & Management, Nanjing
• 2022, Landslide hazard mitigation and early warning system in Hong Kong, The 2022 Summit on Diagnosis and Treatment for Civil Engineering, Shenzhen
• 2021, Probabilistic site characterization and data fusion in geotechnical engineering, The Youth Forum of Engineering Risk and Insurance Research, Shanghai
  
  

🌍 Services

Journal editorial services

• 2024 – present, Assistant Editor & Editorial Board Member, Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards
• 2024 – present, Early Career Editorial Board Member, Intelligent Geoengineering
• 2025, Guest Editor, Geodata and AI
  

Professional societies

• 2022 – present, Member, ASCE Embankments, Dams, and Slopes Committee
• 2022 – present, Corresponding Member, ISSMGE Technical Committees TC304 (Risk) / TC309 (Machine Learning) / TC222 (BIM and Digital Twin)
• 2019 – present, Member, Youth Committee of Risk and Insurance Research Branch of China Civil Engineering Society
• 2024 – present, Early Career Member, Future of Machine Learning in Geotechnics (FOMLIG) Council
• 2021 – present, Member, International Society for Soil Mechanics and Geotechnical Engineering (ISSMGE)
• 2018 – present, Member, International Society for Rock Mechanics
• 2018 – present, Member, China National Committee on Large Dams
• 2024 – present, Member, China Civil Engineering Society
• 2022 – present, Member, Hong Kong Geotechnical Society
• 2017 – present, Associate Member, American Society of Civil Engineers (ASCE)
  

Conference services

• 2025, Secretary of Congress Question, The 28th Congress of International Commission on Large Dams, Chengdu
• 2024, Organizing Committee Secretary, The 2nd Workshop on Future of Machine Learning in Geotechnics, Chengdu
• 2025, Session Chair, The 9th International Symposium for Geotechnical Safety and Risk, Oslo, Norway
• 2023, Session Chair, The XIV Congress of the International Association for Engineering Geology and the Environment, Chengdu
• 2023, Session Chair, ASCE Geo-Risk 2023, Arlington, United States
• 2024, Session Chair, The 6th National Young Scholar Symposium on Engineering Risk Analysis & Management, Nanjing
• 2023, Session Chair, The 8th Youth Scientist Forum of Earth Science, Wuhan
  

Journal reviews

• Géotechnique
• Journal of Geotechnical and Geoenvironmental Engineering
• Canadian Geotechnical Journal
• Engineering Geology
• Landslides
• Acta Geotechnica
• Computers and Geotechnics
• Bulletin of Engineering Geology and the Environment
• Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards
• ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
• Journal of Rock Mechanics and Geotechnical Engineering
  
  

🏆 Honors and Awards

• 2023, Shanghai Magnolia Talents (Youth)
• 2023, ISSMGE Bright Spark Lecture Award, ISSMGE
• 2024, HKIE Geotechnical Paper Award, The Hong Kong Institution of Engineers (HKIE)
• 2023, 75th Géotechnique Anniversary Early Career Award, Journal Géotechnique
• 2018, Georisk Best Paper Award, Journal Georisk
• 2020, Georisk Most Cited Award, Journal Georisk
• 2019, Outstanding Doctoral Thesis in Hydraulic Engineering, China Association of Hydraulic Engineering Education
• 2013, Outstanding Bachelor’s Thesis, Education Board of Hubei Province
• 2021, Frontrunner 5000 - Top Articles in Outstanding S&T Journals of China, Institute of Scientific and Technical Information of China, twice
• 2023, Excellent Paper Award, Joint Workshop on Future of Machine Learning in Geotechnics & Use of Urban Geoinformation for Geotechnical Practice
  
  

👥 Group Activities

• 2025.09: 🎉🎉 Icebreaker Dinner

• 2025.01: 🎉🎉 Happy New Year!!

Copyright © 2025 Te Xiao. All rights reserved.