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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:

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

Showing 1-25 of 42 citing articles:

Prediction of concrete and FRC properties at high temperature using machine and deep learning: A review of recent advances and future perspectives
Nizar Faisal Alkayem, Lei Shen, Ali Mayya, et al.
Journal of Building Engineering (2023) Vol. 83, pp. 108369-108369
Closed Access | Times Cited: 90
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
Data-driven predictive model of coal permeability based on microscopic fracture structure characterization
Tianhao Yan, Xiaomeng Xu, Jiafeng Liu, et al.
Journal of Rock Mechanics and Geotechnical Engineering (2025)
Open Access | Times Cited: 1
A critical analysis of compressive strength prediction of glass fiber and carbon fiber reinforced concrete over machine learning models
K. K. Yaswanth, V. S. Vani, Krupasindhu Biswal, et al.
Multiscale and Multidisciplinary Modeling Experiments and Design (2025) Vol. 8, Iss. 3
Closed Access | Times Cited: 1
Modified particle packing approach for optimizing waste marble powder as a cement substitute in high-performance concrete
Ahmed Essam, Sahar A. Mostafa, Mehran Khan, et al.
Construction and Building Materials (2023) Vol. 409, pp. 133845-133845
Closed Access | Times Cited: 31
Predictive models in machine learning for strength and life cycle assessment of concrete structures
A. Dinesh, B. Rahul Prasad
Automation in Construction (2024) Vol. 162, pp. 105412-105412
Closed Access | Times Cited: 12
Assessment of short and long-term pozzolanic activity of natural pozzolans using machine learning approaches
Jitendra Khatti, Berivan Yılmazer Polat
Structures (2024) Vol. 68, pp. 107159-107159
Closed Access | Times Cited: 11
Prediction of high-performance concrete strength using machine learning with hierarchical regression
Iman Kattoof Harith, Wissam Nadir, Mustafa S. Salah, et al.
Multiscale and Multidisciplinary Modeling Experiments and Design (2024) Vol. 7, Iss. 5, pp. 4911-4922
Closed Access | Times Cited: 7
Soft computing-based prediction models for compressive strength of concrete
Manish Kumar, Rahul Biswas, Divesh Ranjan Kumar, et al.
Case Studies in Construction Materials (2023) Vol. 19, pp. e02321-e02321
Open Access | Times Cited: 19
Harnessing Synergy of Machine Learning and Nature-Inspired Optimization for Enhanced Compressive Strength Prediction in Concrete
Abba Bashir, Esar Ahmad, Shashivendra Dulawat, et al.
Hybrid Advances (2025), pp. 100404-100404
Open Access
Optimized Decision-Making for Tunnel Boring Machine Control Parameters
Zhenliang Zhou, Zonglin Li, Zhongsheng Tan, et al.
Rock Mechanics and Rock Engineering (2025)
Closed Access
Machine Learning for Defect Condition Rating of Wall Wooden Columns in Ancient Buildings
Yufeng Li, Wu Ouyang, Zhenbo Xin, et al.
Case Studies in Construction Materials (2025), pp. e04458-e04458
Open Access
Optimized neural network for soil moisture prediction in precision agriculture
Prity Soni, Rohit Kumar, Sudhanshu Mishra, et al.
Measurement (2025), pp. 117380-117380
Closed Access
Prediction of compressive strength of high-performance concrete using optimization machine learning approaches with SHAP analysis
Md Mahamodul Islam, Pobithra Das, Md Mahbubur Rahman, et al.
Journal of Building Pathology and Rehabilitation (2024) Vol. 9, Iss. 2
Closed Access | Times Cited: 4
An AutoGluon-enabled robust machine learning model for concrete tensile and compressive strength forecast
Chukwuemeka Daniel, Edith Komo Neufville
International Journal of Construction Management (2025), pp. 1-12
Closed Access
How machine learning can transform the future of concrete
Kaoutar Mouzoun, Azzeddine Bouyahyaoui, Hanane Moulay Abdelali, et al.
Asian Journal of Civil Engineering (2025)
Closed Access
Ultrasonic detection and deep learning for high-precision concrete strength prediction
Xu Gan, Wei Wang, Chenhui Jiang, et al.
Journal of Building Engineering (2025), pp. 112372-112372
Closed Access
Hybrid machine learning models for predicting compressive strength of self-compacting concrete: an integration of ANFIS and Metaheuristic algorithm
Somdutta, Baboo Rai
Nondestructive Testing And Evaluation (2025), pp. 1-33
Closed Access
Investigating Landfill Leachate and Groundwater Quality Prediction Using a Robust Integrated Artificial Intelligence Model: Grey Wolf Metaheuristic Optimization Algorithm and Extreme Learning Machine
Meysam Alizamir, Zahra Kazemi, Zohre Kazemi, et al.
Water (2023) Vol. 15, Iss. 13, pp. 2453-2453
Open Access | Times Cited: 11
A data-driven, machine learning scheme used to predict the structural response of masonry arches
Siphesihle Mpho Motsa, Georgios Ε. Stavroulakis, Georgios Α. Drosopoulos
Engineering Structures (2023) Vol. 296, pp. 116912-116912
Closed Access | Times Cited: 7
Predicting mechanical properties of sustainable green concrete using novel machine learning: Stacking and gene expression programming
Muhammad Waqas Ashraf, Adnan Khan, Yongming Tu, et al.
REVIEWS ON ADVANCED MATERIALS SCIENCE (2024) Vol. 63, Iss. 1
Open Access | Times Cited: 2
Anticipation of shear strength of recycled aggregate reinforced concrete beams: a novel hybrid RF-TGC model and realistic implementation
Duy‐Liem Nguyen, Tan‐Duy Phan
Asian Journal of Civil Engineering (2024)
Closed Access | Times Cited: 2
Predicting residual strength of hybrid fibre-reinforced Self-compacting concrete (HFR-SCC) exposed to elevated temperatures using machine learning
Muhammad Saud Khan, Liqiang Ma, Waleed Bin Inqiad, et al.
Case Studies in Construction Materials (2024) Vol. 22, pp. e04112-e04112
Closed Access | Times Cited: 2
Predicting the compressive strength of sulfur concrete using soft computing techniques
Seyed Azim Hosseini, Hossein Maleki Toulabi
Multiscale and Multidisciplinary Modeling Experiments and Design (2023) Vol. 7, Iss. 1, pp. 443-457
Closed Access | Times Cited: 6
Toward presenting an ensemble meta‐model for evaluation of pozzolanic mixtures incorporating industrial by‐products
Ali Ashrafian, Ali Behnood, Emadaldin Mohammadi Golafshani, et al.
Structural Concrete (2023) Vol. 25, Iss. 2, pp. 1305-1323
Closed Access | Times Cited: 5
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