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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

An ensemble method to improve prediction of earthquake-induced soil liquefaction: a multi-dataset study
Junfei Zhang, Yuhang Wang
Neural Computing and Applications (2020) Vol. 33, Iss. 5, pp. 1533-1546
Closed Access | Times Cited: 57

Showing 1-25 of 57 citing articles:

Employing a genetic algorithm and grey wolf optimizer for optimizing RF models to evaluate soil liquefaction potential
Jian Zhou, Shuai Huang, Tao Zhou, et al.
Artificial Intelligence Review (2022) Vol. 55, Iss. 7, pp. 5673-5705
Closed Access | Times Cited: 95
Stacked ensemble model for optimized prediction of triangular side orifice discharge coefficient
Mohamed Kamel Elshaarawy, Abdelrahman Kamal Hamed
Engineering Optimization (2024), pp. 1-31
Closed Access | Times Cited: 17
A hybrid intelligent system for designing optimal proportions of recycled aggregate concrete
Junfei Zhang, Yimiao Huang, Farhad Aslani, et al.
Journal of Cleaner Production (2020) Vol. 273, pp. 122922-122922
Closed Access | Times Cited: 105
Machine-learning-assisted shear strength prediction of reinforced concrete beams with and without stirrups
Junfei Zhang, Yuantian Sun, Guichen Li, et al.
Engineering With Computers (2020) Vol. 38, Iss. 2, pp. 1293-1307
Closed Access | Times Cited: 92
Performance evaluation of hybrid GA–SVM and GWO–SVM models to predict earthquake-induced liquefaction potential of soil: a multi-dataset investigation
Jian Zhou, Shuai Huang, Mingzheng Wang, et al.
Engineering With Computers (2021) Vol. 38, Iss. S5, pp. 4197-4215
Closed Access | Times Cited: 91
A new auto-tuning model for predicting the rock fragmentation: a cat swarm optimization algorithm
Jiandong Huang, Panagiotis G. Asteris, Siavash Manafi Khajeh Pasha, et al.
Engineering With Computers (2020) Vol. 38, Iss. 3, pp. 2209-2220
Closed Access | Times Cited: 77
Comparison of tree-based machine learning algorithms for predicting liquefaction potential using canonical correlation forest, rotation forest, and random forest based on CPT data
Selçuk Demir, Emrehan Kutluğ Şahin
Soil Dynamics and Earthquake Engineering (2021) Vol. 154, pp. 107130-107130
Closed Access | Times Cited: 55
Modelling and validation of liquefaction potential index of fine-grained soils using ensemble learning paradigms
Sufyan Ghani, Sanjog Chhetri Sapkota, Raushan Kumar Singh, et al.
Soil Dynamics and Earthquake Engineering (2023) Vol. 177, pp. 108399-108399
Closed Access | Times Cited: 32
Liquefaction Potential Assessment of Soils Using Machine Learning Techniques: A State-of-the-Art Review from 1994–2021
Kaushik Jas, G. R. Dodagoudar
International Journal of Geomechanics (2023) Vol. 23, Iss. 7
Closed Access | Times Cited: 25
A novel tool for probabilistic modeling of liquefaction behavior in alluvial soil
Sufyan Ghani, Sunita Kumari
Georisk Assessment and Management of Risk for Engineered Systems and Geohazards (2024), pp. 1-24
Closed Access | Times Cited: 9
Dynamic Effect of Landslides Triggered by Earthquake: A Case Study in Moxi Town of Luding County, China
Hongfu Zhou, Fei Ye, Wenxi Fu, et al.
Journal of Earth Science (2024) Vol. 35, Iss. 1, pp. 221-234
Closed Access | Times Cited: 7
Improving the performance of LSSVM model in predicting the safety factor for circular failure slope through optimization algorithms
Zeng Fan, Menad Nait Amar, Ahmed Salih Mohammed, et al.
Engineering With Computers (2021) Vol. 38, Iss. S3, pp. 1755-1766
Closed Access | Times Cited: 41
Prediction of probability of liquefaction using hybrid ANN with optimization techniques
Divesh Ranjan Kumar, Pijush Samui, Avijit Burman
Arabian Journal of Geosciences (2022) Vol. 15, Iss. 20
Closed Access | Times Cited: 35
A Bayesian decision network–based pre-disaster mitigation model for earthquake-induced cascading events to balance costs and benefits on a limited budget
Wenjing Gu, Jiangnan Qiu, Jilei Hu, et al.
Computers & Industrial Engineering (2024) Vol. 191, pp. 110161-110161
Closed Access | Times Cited: 5
Revealing the nature of soil liquefaction using machine learning
Sufyan Ghani, Ishwor Thapa, Amrendra Kumar, et al.
Earth Science Informatics (2025) Vol. 18, Iss. 2
Open Access
Civil Engineering Quality Monitoring System Based on Big Data
Li‐Qun Chen, Song Chen
Sustainable civil infrastructures (2025), pp. 283-293
Closed Access
Bayesian Network Ensemble Models Applied to Seismic Liquefaction Prediction Based on Different In-situ Test Databases
Wenjun Zou, Jilei Hu
Applied Soft Computing (2025), pp. 112668-112668
Closed Access
Deep learning to evaluate seismic-induced soil liquefaction and modified transfer learning between various data sources
Hongwei Guo, Chao Zhang, Hongyuan Fang, et al.
Underground Space (2025)
Open Access
Stability of submarine slopes with monopile foundations under storm conditions
Benjian Song, Cathal Cummins, Qingping Zou
Ocean Engineering (2025) Vol. 323, pp. 120464-120464
Open Access
Estimation of soil liquefaction using artificial intelligence techniques: an extended comparison between machine and deep learning approaches
Eyyüp Hakan Şehmusoğlu, Талас Фикрет Курназ, Caner Erden
Environmental Earth Sciences (2025) Vol. 84, Iss. 5
Open Access
Improving soil liquefaction prediction: A sophisticated ensemble classifier utilizing enhanced correlation features and a metaheuristic ant colony optimization approach
Nerusupalli Dinesh Kumar Reddy, Diksha Diksha, Ashok Kumar Gupta, et al.
Engineering Geology (2025), pp. 108036-108036
Closed Access
Application of machine learning in early warning system of geotechnical disaster: a systematic and comprehensive review
Shan Lin, Zenglong Liang, Hongwei Guo, et al.
Artificial Intelligence Review (2025) Vol. 58, Iss. 6
Open Access
Improved prediction of clay soil expansion using machine learning algorithms and meta-heuristic dichotomous ensemble classifiers
Eyo Eyo, Samuel J. Abbey, T.T. Lawrence, et al.
Geoscience Frontiers (2021) Vol. 13, Iss. 1, pp. 101296-101296
Open Access | Times Cited: 31
Reliability Analysis of Gravity Retaining Wall Using Hybrid ANFIS
Rashid Mustafa, Pijush Samui, Sunita Kumari
Infrastructures (2022) Vol. 7, Iss. 9, pp. 121-121
Open Access | Times Cited: 19
Why “AI” models for predicting soil liquefaction have been ignored, plus some that shouldn’t be
Brett W. Maurer, Morgan Sanger
Earthquake Spectra (2023) Vol. 39, Iss. 3, pp. 1883-1910
Closed Access | Times Cited: 10
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