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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:

Implementing ensemble learning models for the prediction of shear strength of soil
Ahsan Rabbani, Pijush Samui, Sunita Kumari
Asian Journal of Civil Engineering (2023) Vol. 24, Iss. 7, pp. 2103-2119
Closed Access | Times Cited: 29

Showing 1-25 of 29 citing articles:

Predictive modelling of cohesion and friction angle of soil using gene expression programming: a step towards smart and sustainable construction
Muhammad Naqeeb Nawaz, Badee Alshameri, Zain Maqsood, et al.
Neural Computing and Applications (2024) Vol. 36, Iss. 18, pp. 10545-10566
Closed Access | Times Cited: 19
Utilization of Tree-Based Ensemble Models for Predicting the Shear Strength of Soil
Ahsan Rabbani, Jan Afzal Muslih, Mukul Saxena, et al.
Transportation Infrastructure Geotechnology (2024) Vol. 11, Iss. 4, pp. 2382-2405
Closed Access | Times Cited: 13
Evaluating the slope behavior for geophysical flow prediction with advanced machine learning combinations
Kennedy C. Onyelowe, Ahmed M. Ebid, Shadi Hanandeh, et al.
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access | Times Cited: 1
Study on predicting compressive strength of concrete using supervised machine learning techniques
B. Vamsi Varma, Elluri Venkata Prasad, Sudhakar Singha
Asian Journal of Civil Engineering (2023) Vol. 24, Iss. 7, pp. 2549-2560
Closed Access | Times Cited: 22
Optimized ANN-based approach for estimation of shear strength of soil
Ahsan Rabbani, Pijush Samui, Sunita Kumari
Asian Journal of Civil Engineering (2023) Vol. 24, Iss. 8, pp. 3627-3640
Closed Access | Times Cited: 20
Optimization of an Artificial Neural Network Using Four Novel Metaheuristic Algorithms for the Prediction of Rock Fragmentation in Mine Blasting
Ahsan Rabbani, Divesh Ranjan Kumar, Yewuhalashet Fissha, et al.
Journal of The Institution of Engineers (India) Series D (2024)
Closed Access | Times Cited: 7
Data-driven prediction of natural period for existing RC high-rise buildings using probabilistic machine learning methods
Jiazeng Shan, Chenyu Huang, Luji Wang, et al.
Journal of Building Engineering (2024) Vol. 90, pp. 109394-109394
Closed Access | Times Cited: 6
Optimizing soil settlement/consolidation prediction in finland clays: machine learning regressions with bayesian hyperparameter selection
Ahmad Alkhdour, Mahmoud Al Khazaleh, Rakan Al Mnaseer, et al.
Asian Journal of Civil Engineering (2023) Vol. 24, Iss. 8, pp. 3209-3225
Closed Access | Times Cited: 13
Optimization of an Artificial Neural Network Using Three Novel Meta-heuristic Algorithms for Predicting the Shear Strength of Soil
Ahsan Rabbani, Pijush Samui, Sunita Kumari, et al.
Transportation Infrastructure Geotechnology (2023) Vol. 11, Iss. 4, pp. 1708-1729
Closed Access | Times Cited: 12
Applications of machine learning in predicting rut depth in off-road environments
Behzad Golanbari, Aref Mardani, Nashmil Farhadi, et al.
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access
ANN Insight in Forecasting Slope Stability by Analyzing the Influence of Strength Parameters in 2D and 3D Scenarios
Masoud Nasiri, Ehsan Amiri
Transportation Infrastructure Geotechnology (2025) Vol. 12, Iss. 4
Closed Access
Reliability analysis of gravity retaining wall under seismic conditions using a novel hybrid paradigm of ELM and improved grey wolf optimizer
Avinash Kumar, Avijit Burman
Modeling Earth Systems and Environment (2025) Vol. 11, Iss. 3
Closed Access
Advanced machine learning techniques for predicting compressive strength of ultra-high performance concrete
Arslan Qayyum Khan, Syed Ghulam Muhammad, Ali Raza, et al.
Frontiers of Structural and Civil Engineering (2025)
Closed Access
GUI-powered compressive strength estimation of green concrete utilising an efficient ensemble learning paradigm
Subodh Kumar Suman, Shivani Kamal, Sudeep Kumar, et al.
Nondestructive Testing And Evaluation (2025), pp. 1-26
Closed Access
Hybrid Machine Learning Models to Predict the Uniaxial Compressive Strength of Rocks Based on Non-Destructive Tests
Sasan Ghorbani, Ali Bameri
Transportation Infrastructure Geotechnology (2025) Vol. 12, Iss. 5
Closed Access
Impact of waste foundry sand on drainage behavior of sandy soil: an experimental and machine learning study
Ankit Kumar, Aditya Parihar
AI in Civil Engineering (2024) Vol. 3, Iss. 1
Open Access | Times Cited: 3
Artificial neural network modeling for mineralogical and strength analysis of clayey soils
Sayali Rautmare, Aakruti Bhimpure, R. S. Dalvi, et al.
Multiscale and Multidisciplinary Modeling Experiments and Design (2025) Vol. 8, Iss. 2
Closed Access
Optimization of artificial neural network for predicting radon exhalation rates and effective radium content in soil samples
Selin Erzin
Earth Science Informatics (2025) Vol. 18, Iss. 1
Closed Access
Predicting macro-mechanical properties of loess from basic physical properties using various machine learning methods
Yongfeng Zhu, Wei Xiong, Wen Fan, et al.
Environmental Earth Sciences (2025) Vol. 84, Iss. 10
Closed Access
Assessing the shear strength of sandy soil reinforced with polyethylene-terephthalate: an AI-based approach
Masoud Samaei, Morteza Alinejad Omran, Mohsen Keramati, et al.
Earth Science Informatics (2024) Vol. 17, Iss. 5, pp. 4507-4526
Closed Access | Times Cited: 2
Assessment of leachate-contaminated clays using experimental and artificial methods
Hossein Moradi Moghaddam, Ahmad Fahimifar, Taghi Ebadi, et al.
Journal of Rock Mechanics and Geotechnical Engineering (2024)
Open Access | Times Cited: 2
Prediction of unconfined compressive strength of cement–lime stabilized soil using artificial neural network
Ajay Kumar, Vikash Singh, Sumit Singh, et al.
Asian Journal of Civil Engineering (2023) Vol. 25, Iss. 2, pp. 2229-2246
Closed Access | Times Cited: 5
Hierarchical Pseudo-Continuous Machine-Learning-Based Pedotransfer Models for Infiltration Curves: An Investigation on the Role of Regularization and Ensemble Modeling
Mahdi Selahvarzi, Seyed Mohammadreza Naghedifar, Arman Oliazadeh, et al.
(2024)
Closed Access | Times Cited: 1
GIS Applications and Machine Learning Approaches in Civil Engineering
N. R. Asha Rani, Sasmita Bal, M. Inayathulla
Lecture notes in civil engineering (2024), pp. 157-166
Closed Access
Global Research Trends in Soft Soil Management for Infrastructure Development: Opportunities and Challenges
L. Sim, Herda Yati Binti Katman, Intan Nor Zuliana Baharuddin, et al.
IEEE Access (2024) Vol. 12, pp. 73731-73751
Open Access
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