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OpenAlex Citation Counts

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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:

Machine Learning-Based Modelling of Soil Properties for Geotechnical Design: Review, Tool Development and Comparison
Pin Zhang, Zhen‐Yu Yin, Yin-Fu Jin
Archives of Computational Methods in Engineering (2021) Vol. 29, Iss. 2, pp. 1229-1245
Closed Access | Times Cited: 61

Showing 26-50 of 61 citing articles:

Digital mapping for soil texture class prediction in northwestern Türkiye by different machine learning algorithms
Fuat Kaya, Levent Başayığıt, Ali Keshavarzi, et al.
Geoderma Regional (2022) Vol. 31, pp. e00584-e00584
Closed Access | Times Cited: 19
A neural network-based material cell for elastoplasticity and its performance in FE analyses of boundary value problems
Shaoheng Guan, Xue Zhang, Sascha Ranftl, et al.
International Journal of Plasticity (2023) Vol. 171, pp. 103811-103811
Open Access | Times Cited: 11
Leading-edge Artificial Intelligence (AI) and Internet of Things (IoT) technologies for enhanced geotechnical site characterization
Nitin Liladhar Rane, Saurabh Choudhary, Jayesh Rane
SSRN Electronic Journal (2023)
Closed Access | Times Cited: 10
Evaluation of empirical and machine learning models for predicting shear wave velocity of granular soils based on laboratory element tests
Zohreh Mousavi, Meysam Bayat, Jun Yang, et al.
Soil Dynamics and Earthquake Engineering (2024) Vol. 183, pp. 108805-108805
Closed Access | Times Cited: 3
A Comparative Analysis of Hybrid Computational Models Constructed with Swarm Intelligence Algorithms for Estimating Soil Compression Index
Abidhan Bardhan, Navid Kardani, Abdel Kareem Alzo’ubi, et al.
Archives of Computational Methods in Engineering (2022) Vol. 29, Iss. 7, pp. 4735-4773
Closed Access | Times Cited: 18
Development of an LSTM-based model for predicting the long-term settlement of land reclamation and a GUI-based tool
Xiaoxuan Chen, Jie Yang, Geng‐Fu He, et al.
Acta Geotechnica (2023) Vol. 18, Iss. 7, pp. 3849-3862
Closed Access | Times Cited: 9
Prediction of the slurry pressure and inversion of formation characteristics based on a machine learning algorithm during tunnelling in a fault fracture zone
Xiang Shen, Yifan Chen, Liqiang Cao, et al.
Tunnelling and Underground Space Technology (2023) Vol. 144, pp. 105514-105514
Closed Access | Times Cited: 9
Prediction of collapsibility of loess site based on artificial intelligence: comparison of different algorithms
Xueliang Zhu, Shuai Shao, Shengjun Shao
Environmental Earth Sciences (2024) Vol. 83, Iss. 3
Closed Access | Times Cited: 3
Machine Learning-Based Prediction of Unconfined Compressive Strength of Sands Treated by Microbially-Induced Calcite Precipitation (MICP): A Gradient Boosting Approach and Correlation Analysis
Saeed Talamkhani
Advances in Civil Engineering (2023) Vol. 2023, pp. 1-20
Open Access | Times Cited: 8
Ultrasonic assessment of cement-stabilized soils: Deep learning experimental results
Janusz Kozubal, Tomasz Kania, Ahmad S. Tarawneh, et al.
Measurement (2023) Vol. 223, pp. 113793-113793
Open Access | Times Cited: 8
Ready-to-use deep-learning surrogate models for problems with spatially variable inputs and outputs
Xuzhen He, Haoding Xu, Daichao Sheng
Acta Geotechnica (2022) Vol. 18, Iss. 4, pp. 1681-1698
Open Access | Times Cited: 12
An XGBOOST predictive model of void ratio in sandy soils with shear-wave velocity as major input
Yunhan Huang, Yaning Wang, Peipei Wang, et al.
Transportation Geotechnics (2023) Vol. 42, pp. 101100-101100
Closed Access | Times Cited: 7
Herramientas digitales y uso de inteligencia artificial en geotecnia: Un enfoque en la evaluación de taludes con Matlab
José Ramón Toro López
LATAM Revista Latinoamericana de Ciencias Sociales y Humanidades (2024) Vol. 5, Iss. 1
Open Access | Times Cited: 2
Enhancing clay content estimation through hybrid CatBoost-GP with model class selection
Weihang Chen, Xing Wan, Jianwen Ding, et al.
Transportation Geotechnics (2024) Vol. 45, pp. 101232-101232
Closed Access | Times Cited: 2
A combined machine learning/search algorithm-based method for the identification of constitutive parameters from laboratory tests and in-situ tests
Changjian Zhou, Bin Gao, Bin Yan, et al.
Computers and Geotechnics (2024) Vol. 170, pp. 106268-106268
Closed Access | Times Cited: 2
Investigating a hybrid extreme learning machine coupled with Dingo Optimization Algorithm for modeling liquefaction triggering in sand-silt mixtures
Mohammed Majeed Hameed, Adil Masood, Aman Srivastava, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 2
Machine Learning Models for Predicting Shear Wave Velocity of Soils
Zohreh Mousavi, Meysam Bayat, Wei-Qiang Feng
IOP Conference Series Earth and Environmental Science (2024) Vol. 1334, Iss. 1, pp. 012039-012039
Open Access | Times Cited: 2
Application of Machine Learning in Geotechnical Engineering for Risk Assessment
Ali Akbar Firoozi, Ali Asghar Firoozi
Artificial intelligence (2023)
Open Access | Times Cited: 6
Explicit soft computing model to predict the undrained bearing capacity of footing resting on aggregate pier reinforced cohesive ground
Saif Alzabeebee
Innovative Infrastructure Solutions (2021) Vol. 7, Iss. 1
Closed Access | Times Cited: 15
Intelligent Analysis of Construction Costs of Shield Tunneling in Complex Geological Conditions by Machine Learning Method
Xiaomu Ye, Pengfei Ding, Dawei Jin, et al.
Mathematics (2023) Vol. 11, Iss. 6, pp. 1423-1423
Open Access | Times Cited: 5
一种数据驱动隧道掘进机推力的建模与预测方法
Lintao Wang, Fengzhang Zhu, Jie Li, et al.
Journal of Zhejiang University. Science A (2023) Vol. 24, Iss. 9, pp. 801-816
Closed Access | Times Cited: 5
Back-calculation of soil parameters from displacement-controlled cavity expansion under geostatic stress by FEM and machine learning
Fernando Patino-Ramirez, Zijie Jay Wang, Duen Horng Chau, et al.
Acta Geotechnica (2022) Vol. 18, Iss. 4, pp. 1755-1768
Open Access | Times Cited: 8
Slope multi-step excavation displacement prediction surrogate model based on a long short-term memory neural network: for small sample data and multi-feature multi-task learning
Yue Dai, Wujiao Dai, Jiawei Xie
Georisk Assessment and Management of Risk for Engineered Systems and Geohazards (2024), pp. 1-20
Closed Access | Times Cited: 1
Multi‐Scale Geomechanical Modelling of Unconventional Shale Gas: The Implication on Assisting Geophysics–Geology–Engineering Integration
Yue Xiao, Chong Liang, Dawei Zhu, et al.
International Journal of Energy Research (2024) Vol. 2024, Iss. 1
Open Access | Times Cited: 1
Mechanical characterization of marl soil treated by cement and lignosulfonate under freeze–thaw cycles: experimental studies and machine-learning modeling
Ali Shafiei, Mohammad Aminpour, Hadi Hasanzadehshooiili, et al.
Bulletin of Engineering Geology and the Environment (2023) Vol. 82, Iss. 6
Closed Access | Times Cited: 4

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