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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:

Soft-computing techniques for prediction of soils consolidation coefficient
Manh Duc Nguyen, Binh Thai Pham, Lanh Si Ho, et al.
CATENA (2020) Vol. 195, pp. 104802-104802
Closed Access | Times Cited: 55

Showing 1-25 of 55 citing articles:

Predicting the settlement of geosynthetic-reinforced soil foundations using evolutionary artificial intelligence technique
Muhammad Nouman Amjad Raja, Sanjay Kumar Shukla
Geotextiles and Geomembranes (2021) Vol. 49, Iss. 5, pp. 1280-1293
Open Access | Times Cited: 131
A novel integrated approach of augmented grey wolf optimizer and ANN for estimating axial load carrying-capacity of concrete-filled steel tube columns
Abidhan Bardhan, Rahul Biswas, Navid Kardani, et al.
Construction and Building Materials (2022) Vol. 337, pp. 127454-127454
Closed Access | Times Cited: 94
Application of multi-algorithm ensemble methods in high-dimensional and small-sample data of geotechnical engineering: A case study of swelling pressure of expansive soils
Chao Li, Lei Wang, Jie Li, et al.
Journal of Rock Mechanics and Geotechnical Engineering (2024) Vol. 16, Iss. 5, pp. 1896-1917
Open Access | Times Cited: 26
Metaheuristic optimization of Levenberg–Marquardt-based artificial neural network using particle swarm optimization for prediction of foamed concrete compressive strength
Haï-Bang Ly, May Huu Nguyen, Binh Thai Pham
Neural Computing and Applications (2021) Vol. 33, Iss. 24, pp. 17331-17351
Closed Access | Times Cited: 105
Spatio-temporal feature fusion for real-time prediction of TBM operating parameters: A deep learning approach
Xianlei Fu, Limao Zhang
Automation in Construction (2021) Vol. 132, pp. 103937-103937
Closed Access | Times Cited: 71
Machine learning approach for predicting and evaluating California bearing ratio of stabilized soil containing industrial waste
Lanh Si Ho, Van Quan Tran
Journal of Cleaner Production (2022) Vol. 370, pp. 133587-133587
Closed Access | Times Cited: 37
Data-driven joint multi-objective prediction and optimization for advanced control during tunnel construction
Xianlei Fu, Maozhi Wu, Robert Lee Kong Tiong, et al.
Expert Systems with Applications (2023) Vol. 238, pp. 122118-122118
Closed Access | Times Cited: 17
Assessing the strength of deep-sea surface ultrasoft sediments with T-bar penetration: A machine learning approach
Xingsen Guo, Xiangshuai Meng, Fei Han, et al.
Engineering Geology (2024) Vol. 338, pp. 107632-107632
Closed Access | Times Cited: 8
Backpropagation Neural Network-Based Machine Learning Model for Prediction of Soil Friction Angle
Thuy‐Anh Nguyen, Haï-Bang Ly, Binh Thai Pham
Mathematical Problems in Engineering (2020) Vol. 2020, pp. 1-11
Open Access | Times Cited: 44
A Novel Hybrid Model Based on a Feedforward Neural Network and One Step Secant Algorithm for Prediction of Load-Bearing Capacity of Rectangular Concrete-Filled Steel Tube Columns
Quang Hung Nguyen, Haï-Bang Ly, Van Quan Tran, et al.
Molecules (2020) Vol. 25, Iss. 15, pp. 3486-3486
Open Access | Times Cited: 43
Application of hybrid intelligent systems in predicting the unconfined compressive strength of clay material mixed with recycled additive
Mohammed Ali Mohammed Al-Bared, Zahiraniza Mustaffa, Danial Jahed Armaghani, et al.
Transportation Geotechnics (2021) Vol. 30, pp. 100627-100627
Closed Access | Times Cited: 35
Predicting uniaxial tensile strength of expansive soil with ensemble learning methods
Yang Chen, Yongfu Xu, Babak Jamhiri, et al.
Computers and Geotechnics (2022) Vol. 150, pp. 104904-104904
Closed Access | Times Cited: 28
Hybrid ANN models for durability of GFRP rebars in alkaline concrete environment using three swarm-based optimization algorithms
Kaffayatullah Khan, Mudassir Iqbal, Fazal E. Jalal, et al.
Construction and Building Materials (2022) Vol. 352, pp. 128862-128862
Closed Access | Times Cited: 28
Prediction of Ultimate Load of Rectangular CFST Columns Using Interpretable Machine Learning Method
Tien-Thinh Le, Hieu Chi Phan
Advances in Civil Engineering (2020) Vol. 2020, Iss. 1
Open Access | Times Cited: 34
Prediction of coefficient of consolidation in soil using machine learning techniques
Mamta Mittal, Suresh Chandra Satapathy, Vaibhav Pal, et al.
Microprocessors and Microsystems (2021) Vol. 82, pp. 103830-103830
Closed Access | Times Cited: 29
A novel machine learning approach for rice yield estimation
Surabhi Lingwal, Komal Kumar Bhatia, Manjeet Singh
Journal of Experimental & Theoretical Artificial Intelligence (2022), pp. 1-20
Closed Access | Times Cited: 23
Deep Learning Approach on Prediction of Soil Consolidation Characteristics
Mintae Kim, Muharrem A. Senturk, Rabia Korkmaz Tan, et al.
Buildings (2024) Vol. 14, Iss. 2, pp. 450-450
Open Access | Times Cited: 4
Forecasting mortality rates using hybrid Lee–Carter model, artificial neural network and random forest
Wei Hong, Jia Hui Yap, Ganeshsree Selvachandran, et al.
Complex & Intelligent Systems (2020) Vol. 7, Iss. 1, pp. 163-189
Open Access | Times Cited: 28
Appraisal of Numerous Machine Learning Techniques for the Prediction of Consolidation Settlement Subjected to Placing of the Fill
Rashid Mustafa, Krishna Kumar, Ravi Shankar
Transportation Infrastructure Geotechnology (2025) Vol. 12, Iss. 4
Closed Access
Probabilistic modeling of surface effects in nano-reinforced materials
Tien-Thinh Le
Computational Materials Science (2020) Vol. 186, pp. 109987-109987
Open Access | Times Cited: 26
A Study on Developing a Model for Predicting the Compression Index of the South Coast Clay of Korea Using Statistical Analysis and Machine Learning Techniques
Sungyeol Lee, Jae‐Mo Kang, Jin‐Young Kim, et al.
Applied Sciences (2024) Vol. 14, Iss. 3, pp. 952-952
Open Access | Times Cited: 3
Prediction and validation of geogrid tensile force distribution in back-to-back MSE walls under rail axle load: finite-element and intelligent techniques
Shilpa S. Vadavadagi, Sowmiya Chawla, P Kumar
Environmental Earth Sciences (2024) Vol. 83, Iss. 5
Closed Access | Times Cited: 3
Deep learning-based prediction of particle breakage and friction angle of water-degradable geomaterials
Mubashir Aziz, Anwaruddin Siddiqui Mohammed, Umair Ali, et al.
Powder Technology (2024) Vol. 444, pp. 120049-120049
Closed Access | Times Cited: 3
Review and comparison of machine learning methods in developing optimal models for predicting geotechnical properties with consideration of feature selection
Tengyuan Zhao, Fenglin Shen, Xu Ling
SOILS AND FOUNDATIONS (2024) Vol. 64, Iss. 6, pp. 101523-101523
Closed Access | Times Cited: 3
Predicting Marshall Stability of Carbon Fiber-Reinforced Asphalt Concrete Using Machine Learning Techniques
Ankita Upadhya, Mohindra Singh Thakur, Parveen Sihag
International Journal of Pavement Research and Technology (2022) Vol. 17, Iss. 1, pp. 102-122
Closed Access | Times Cited: 13
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