Leaderboards/Geotechnical Engineering and Analysis

Geotechnical Engineering and Analysis

Last updated July 7, 2026

Rising Stars

New and hot

Rising Stars Researchers

1
Thy Truc Doan
An Giang University
2
Mahdi Rasouli Maleki
Amirkabir University of Technology
3
Divesh Ranjan Kumar
Chulalongkorn University
4
Maaz Amjad
Texas Tech University
5
Kaushik Jas
Hokkaido University
6
Dongku Kim
Korea Institute of Civil Engineering and Building Technology
7
S.C. Jong
Griffith University
8
Da Hu
Hunan City University
9
Zening Zhao
Hong Kong University of Science and Technology
10
Liang Lv
China University of Geosciences

Rising Stars Papers

1
Materials Today: Proceedings
SSRN Electronic Journal|2025
1.4k cit.
2
Machine Learning–Finite Element Mesh Optimization-Based Modeling and Prediction of Excavation-Induced Shield Tunnel Ground Settlement
International Journal of Computational Methods|2024
77 cit.
3
Evaluation and analysis of liquefaction potential of gravelly soils using explainable probabilistic machine learning model
Computers and Geotechnics|2024
61 cit.
4
Prediction method of ground settlement for rectangular tunnel construction
Tunnelling and Underground Space Technology|2025
50 cit.
5
Machine learning-based prediction model for disc cutter life in TBM excavation through hard rock formations
Tunnelling and Underground Space Technology|2024
35 cit.
6
A novel Bayesian network approach for predicting soil-structure interactions induced by deep excavations
Tunnelling and Underground Space Technology|2024
32 cit.
7
Calculation Methods for the Jacking Force of a Rectangular Pipe Jacking Tunnel: Overview and Prospects
Journal of Pipeline Systems Engineering and Practice|2025
30 cit.
8
Robust Geotechnical Design of Piles Incorporating System Reliability
Journal of Geotechnical and Geoenvironmental Engineering|2025
24 cit.
9
Probabilistic capacity energy-based machine learning models for soil liquefaction reliability analysis
Engineering Geology|2024
23 cit.
10
Optimized machine learning models for predicting the tensile strength of high-performance concrete
Journal of Structural Integrity and Maintenance|2025
19 cit.

Emerging Leaders

Gaining momentum

Emerging Leaders Researchers

1
Haijia Wen
Chongqing University of Technology
2
Hai‐Bang Ly
University Of Transport Technology
3
Jianquan Ma
Xi'an University of Science and Technology
4
Yuwei Zhang
Xi'an University of Architecture and Technology
5
Rui Pang
Henan University of Technology
6
Luqi Wang
Chinese Academy of Medical Sciences & Peking Union Medical College
7
Hongwen Jing
China University of Mining and Technology
8
Jinhui Li
Harbin Institute of Technology
9
Chun Zhu
North China Electric Power University
10
Ziquan Chen
Southwest Jiaotong University

Emerging Leaders Papers

1
Deep learning methods for time-dependent reliability analysis of reservoir slopes in spatially variable soils
Computers and Geotechnics|2023
105 cit.
2
Volumetric deformation and damage evolution of Tibet interbedded skarn under multistage constant-amplitude-cyclic loading
International Journal of Rock Mechanics and Mining Sciences|2022
104 cit.
3
Quantification of model uncertainty and variability for landslide displacement prediction based on Monte Carlo simulation
Gondwana Research|2023
102 cit.
4
Development of machine learning methods to predict the compressive strength of fiber-reinforced self-compacting concrete and sensitivity analysis
Construction and Building Materials|2023
98 cit.
5
Experimental study on the shear performance of quasi-NPR steel bolted rock joints
Journal of Rock Mechanics and Geotechnical Engineering|2022
84 cit.
6
Nonlinear creep model of salt rock used for displacement prediction of salt cavern gas storage
Journal of Energy Storage|2022
82 cit.
7
Microstructural variations and damage evolvement of salt rock under cyclic loading
International Journal of Rock Mechanics and Mining Sciences|2022
80 cit.
8
Stochastic Mainshock–Aftershock Simulation and Its Applications in Dynamic Reliability of Structural Systems via DPIM
Journal of Engineering Mechanics|2022
70 cit.
9
Landslide Susceptibility Mapping Based on Interpretable Machine Learning from the Perspective of Geomorphological Differentiation
Land|2023
69 cit.
10
Toward improved prediction of recycled brick aggregate concrete compressive strength by designing ensemble machine learning models
Construction and Building Materials|2023
63 cit.

Current Senior Leaders

Established & currently active

Current Senior Leaders Researchers

1
Shui‐Long Shen
Shantou University
2
Kok‐Kwang Phoon
Singapore University of Technology and Design
3
Daniel Dias
Institut National des Sciences Appliquées de Lyon
4
Scott W. Sloan
University of Newcastle Australia
5
Limin Zhang
Peking University
6
Wengang Zhang
Chinese PLA General Hospital
7
E.T. Brown
Commonwealth Scientific and Industrial Research Organisation
8
Biswajeet Pradhan
University of Technology Sydney
9
Zhen‐Yu Yin
Hong Kong Polytechnic University
10
Gordon A. Fenton
Dalhousie University

Current Senior Leaders Papers

1
Practical estimates of rock mass strength
International Journal of Rock Mechanics and Mining Sciences|1997
3.3k cit.
2
Characterization of geotechnical variability
Canadian Geotechnical Journal|1999
2.3k cit.
3
Empirical Strength Criterion for Rock Masses
Journal of the Geotechnical Engineering Division|1980
1.7k cit.
4
A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS
Computers & Geosciences|2012
1.2k cit.
5
The Hoek–Brown failure criterion and GSI – 2018 edition
Journal of Rock Mechanics and Geotechnical Engineering|2018
1.1k cit.
6
Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models
Landslides|2006
1.1k cit.
7
Probabilistic Slope Stability Analysis by Finite Elements
Journal of Geotechnical and Geoenvironmental Engineering|2004
1.1k cit.
8
Landslide susceptibility assessment and factor effect analysis: backpropagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling
Environmental Modelling & Software|2009
956 cit.
9
Prediction of undrained shear strength using extreme gradient boosting and random forest based on Bayesian optimization
Geoscience Frontiers|2020
844 cit.
10
Evaluation of geotechnical property variability
Canadian Geotechnical Journal|1999
775 cit.

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