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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:

Prediction of cement-based mortars compressive strength using machine learning techniques
Panagiotis G. Asteris, Mohammadreza Koopialipoor, Danial Jahed Armaghani, et al.
Neural Computing and Applications (2021) Vol. 33, Iss. 19, pp. 13089-13121
Closed Access | Times Cited: 175

Showing 1-25 of 175 citing articles:

Convolution-based ensemble learning algorithms to estimate the bond strength of the corroded reinforced concrete
Liborio Cavaleri, Mohammad Sadegh Barkhordari, Constantinos C. Repapis, et al.
Construction and Building Materials (2022) Vol. 359, pp. 129504-129504
Closed Access | Times Cited: 99
Slope Stability Classification under Seismic Conditions Using Several Tree-Based Intelligent Techniques
Panagiotis G. Asteris, Fariz Iskandar Mohd Rizal, Mohammadreza Koopialipoor, et al.
Applied Sciences (2022) Vol. 12, Iss. 3, pp. 1753-1753
Open Access | Times Cited: 82
Assessment of mechanical properties with machine learning modeling and durability, and microstructural characteristics of a biochar-cement mortar composite
Md. Habibur Rahman Sobuz, M.H. Khan, Md. Kawsarul Islam Kabbo, et al.
Construction and Building Materials (2023) Vol. 411, pp. 134281-134281
Closed Access | Times Cited: 55
Compressive strength of concrete material using machine learning techniques
Satish Paudel, Anil Pudasaini, Rajesh Kumar Shrestha, et al.
Cleaner Engineering and Technology (2023) Vol. 15, pp. 100661-100661
Open Access | Times Cited: 49
Mapping the strength of agro-ecological lightweight concrete containing oil palm by-product using artificial intelligence techniques
Ali Ashrafian, Elahe Panahi, Sajjad Salehi, et al.
Structures (2023) Vol. 48, pp. 1209-1229
Closed Access | Times Cited: 42
Efficient machine learning method for evaluating compressive strength of cement stabilized soft soil
Chen Zhang, Zhiduo Zhu, Fa Liu, et al.
Construction and Building Materials (2023) Vol. 392, pp. 131887-131887
Closed Access | Times Cited: 42
Estimating compressive strength of concrete containing rice husk ash using interpretable machine learning-based models
Mana Alyami, Roz‐Ud‐Din Nassar, Majid Khan, et al.
Case Studies in Construction Materials (2024) Vol. 20, pp. e02901-e02901
Open Access | Times Cited: 35
Hybrid nonlinear regression model versus MARS, MEP, and ANN to evaluate the effect of the size and content of waste tire rubber on the compressive strength of concrete
Dilshad Kakasor Ismael Jaf, Aso A. Abdalla, Ahmed Salih Mohammed, et al.
Heliyon (2024) Vol. 10, Iss. 4, pp. e25997-e25997
Open Access | Times Cited: 21
Assessment of the uniaxial compressive strength of intact rocks: an extended comparison between machine and advanced machine learning models
Jitendra Khatti, Kamaldeep Singh Grover
Multiscale and Multidisciplinary Modeling Experiments and Design (2024) Vol. 7, Iss. 4, pp. 3301-3325
Closed Access | Times Cited: 17
Assessment of hydraulic conductivity of compacted clayey soil using artificial neural network: an investigation on structural and database multicollinearity
Jitendra Khatti, Kamaldeep Singh Grover
Earth Science Informatics (2024)
Closed Access | Times Cited: 14
Daily River flow Simulation Using Ensemble Disjoint Aggregating M5-Prime Model
Khabat Khosravi, Nasrin Fathollahzadeh Attar, Sayed M. Bateni, et al.
Heliyon (2024) Vol. 10, Iss. 20, pp. e37965-e37965
Open Access | Times Cited: 14
Stacked-based machine learning to predict the uniaxial compressive strength of concrete materials
Abdelrahman Kamal Hamed, Mohamed Kamel Elshaarawy, Mostafa M. Alsaadawi
Computers & Structures (2025) Vol. 308, pp. 107644-107644
Closed Access | Times Cited: 2
Estimating compressive strength of modern concrete mixtures using computational intelligence: A systematic review
Itzel Nunez, Afshin Marani, Majdi Flah, et al.
Construction and Building Materials (2021) Vol. 310, pp. 125279-125279
Closed Access | Times Cited: 84
Developing bearing capacity model for geogrid-reinforced stone columns improved soft clay utilizing MARS-EBS hybrid method
Ali Reza Ghanizadeh, Afshin Ghanizadeh, Panagiotis G. Asteris, et al.
Transportation Geotechnics (2022) Vol. 38, pp. 100906-100906
Closed Access | Times Cited: 57
Prediction of Interface Bond Strength Between Ultra-High-Performance Concrete (UHPC) and Normal Strength Concrete (NSC) Using a Machine Learning Approach
Abdulwarith Ibrahim Bibi Farouk, Jinsong Zhu
Arabian Journal for Science and Engineering (2022) Vol. 47, Iss. 4, pp. 5337-5363
Closed Access | Times Cited: 45
A Comparative Analysis of Machine Learning Models in Prediction of Mortar Compressive Strength
R. Gayathri, Shola Usharani, Lenka Čepová, et al.
Processes (2022) Vol. 10, Iss. 7, pp. 1387-1387
Open Access | Times Cited: 43
Efficient models to evaluate the effect of C3S, C2S, C3A, and C4AF contents on the long-term compressive strength of cement paste
Ahmed Ahmed, Payam Abubakr, Ahmed Salih Mohammed
Structures (2022) Vol. 47, pp. 1459-1475
Closed Access | Times Cited: 42
Predicting the Compressive Strength of the Cement-Fly Ash–Slag Ternary Concrete Using the Firefly Algorithm (FA) and Random Forest (RF) Hybrid Machine-Learning Method
Jiandong Huang, Mohanad Muayad Sabri Sabri, Dmitrii Vladimirovich Ulrikh, et al.
Materials (2022) Vol. 15, Iss. 12, pp. 4193-4193
Open Access | Times Cited: 39
Estimation of Settlement of Pile Group in Clay Using Soft Computing Techniques
Jitendra Khatti, Hanan Samadi, Kamaldeep Singh Grover
Geotechnical and Geological Engineering (2023) Vol. 42, Iss. 3, pp. 1729-1760
Closed Access | Times Cited: 37
Bayesian machine learning-aided approach bridges between dynamic elasticity and compressive strength in the cement-based mortars
Ning Wang, Majid Samavatian, Vahid Samavatian, et al.
Materials Today Communications (2023) Vol. 35, pp. 106283-106283
Closed Access | Times Cited: 25
Decision tree models for the estimation of geo-polymer concrete compressive strength
Ji Zhou, Zhanlin Su, Shahab Hosseini, et al.
Mathematical Biosciences & Engineering (2023) Vol. 21, Iss. 1, pp. 1413-1444
Open Access | Times Cited: 25
Interpretable Machine Learning for Prediction of Post-Fire Self-Healing of Concrete
Magdalena Rajczakowska, Maciej Szeląg, Karin Habermehl-Cwirzen, et al.
Materials (2023) Vol. 16, Iss. 3, pp. 1273-1273
Open Access | Times Cited: 23
Prediction of bending strength of glass fiber reinforced methacrylate-based pipeline UV-CIPP rehabilitation materials based on machine learning
Yangyang Xia, Chao Zhang, Cuixia Wang, et al.
Tunnelling and Underground Space Technology (2023) Vol. 140, pp. 105319-105319
Closed Access | Times Cited: 22
State-of-the-art XGBoost, RF and DNN based soft-computing models for PGPN piles
Manish Kumar, Pijush Samui, Divesh Ranjan Kumar, et al.
Geomechanics and Geoengineering (2024) Vol. 19, Iss. 6, pp. 975-990
Closed Access | Times Cited: 13
Prediction of Uniaxial Strength of Rocks Using Relevance Vector Machine Improved with Dual Kernels and Metaheuristic Algorithms
Jitendra Khatti, Kamaldeep Singh Grover
Rock Mechanics and Rock Engineering (2024) Vol. 57, Iss. 8, pp. 6227-6258
Closed Access | Times Cited: 12
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