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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:

Predictive models for concrete properties using machine learning and deep learning approaches: A review
Mohammad Mohtasham Moein, Ashkan Saradar, Komeil Rahmati, et al.
Journal of Building Engineering (2022) Vol. 63, pp. 105444-105444
Open Access | Times Cited: 225

Showing 1-25 of 225 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: 88
Prediction of compressive strength of geopolymer concrete using a hybrid ensemble of grey wolf optimized machine learning estimators
Suraj Kumar Parhi, Sanjaya Kumar Patro
Journal of Building Engineering (2023) Vol. 71, pp. 106521-106521
Closed Access | Times Cited: 74
Optimization design for alkali-activated slag-fly ash geopolymer concrete based on artificial intelligence considering compressive strength, cost, and carbon emission
Yue Li, Jiale Shen, Hui Lin, et al.
Journal of Building Engineering (2023) Vol. 75, pp. 106929-106929
Closed Access | Times Cited: 66
Development of machine learning methods to predict the compressive strength of fiber-reinforced self-compacting concrete and sensitivity analysis
Hai‐Van Thi, May Huu Nguyen, Hai‐Bang Ly
Construction and Building Materials (2023) Vol. 367, pp. 130339-130339
Closed Access | Times Cited: 62
Prediction and optimization model of sustainable concrete properties using machine learning, deep learning and swarm intelligence: A review
Shiqi Wang, Peng Xia, Keyu Chen, et al.
Journal of Building Engineering (2023) Vol. 80, pp. 108065-108065
Closed Access | Times Cited: 57
Machine Learning-Based Method for Predicting Compressive Strength of Concrete
Daihong Li, Zhili Tang, Qian Kang, et al.
Processes (2023) Vol. 11, Iss. 2, pp. 390-390
Open Access | Times Cited: 50
Hybrid intelligence models for compressive strength prediction of MPC composites and parametric analysis with SHAP algorithm
M. Aminul Haque, Bing Chen, Abul Kashem, et al.
Materials Today Communications (2023) Vol. 35, pp. 105547-105547
Closed Access | Times Cited: 42
Reliability analysis and experimental investigation of impact resistance of concrete reinforced with polyolefin fiber in different shapes, lengths, and doses
Mohammad Mohtasham Moein, Ashkan Saradar, Komeil Rahmati, et al.
Journal of Building Engineering (2023) Vol. 69, pp. 106262-106262
Closed Access | Times Cited: 39
Investigation of optimized machine learning models with PSO for forecasting the shear capacity of steel fiber-reinforced SCC beams with/out stirrups
Faruk Ergen, Metin Katlav
Journal of Building Engineering (2024) Vol. 83, pp. 108455-108455
Closed Access | Times Cited: 27
A Critical Review Examining the Characteristics of Modified Concretes with Different Nanomaterials
Mohammad Mohtasham Moein, Komeil Rahmati, Ashkan Saradar, et al.
Materials (2024) Vol. 17, Iss. 2, pp. 409-409
Open Access | Times Cited: 21
Predictive Modeling and Experimental Validation for Assessing the Mechanical Properties of Cementitious Composites Made with Silica Fume and Ground Granulated Blast Furnace Slag
Usama Asif, Shazim Ali Memon, Muhammad Faisal Javed, et al.
Buildings (2024) Vol. 14, Iss. 4, pp. 1091-1091
Open Access | Times Cited: 17
Prediction of mechanical properties of high‐performance concrete and ultrahigh‐performance concrete using soft computing techniques: A critical review
Rakesh Kumar, Baboo Rai, Pijush Samui
Structural Concrete (2024)
Closed Access | Times Cited: 16
An enhanced asynchronous advantage actor-critic-based algorithm for performance optimization in mobile edge computing -enabled internet of vehicles networks
Komeil Moghaddasi, Shakiba Rajabi, Farhad Soleimanian Gharehchopogh
Peer-to-Peer Networking and Applications (2024) Vol. 17, Iss. 3, pp. 1169-1189
Closed Access | Times Cited: 15
The state-of-the-art review on biochar as green additives in cementitious composites: performance, applications, machine learning predictions, and environmental and economic implications
Ping Ye, Binglin Guo, Huyong Qin, et al.
Biochar (2025) Vol. 7, Iss. 1
Open Access | Times Cited: 1
Experimental and AI-driven enhancements in gas-phase photocatalytic CO2 conversion over synthesized highly ordered anodic TiO2 nanotubes
Md. Arif Hossen, Md Munirul Hasan, Yunus Ahmed, et al.
Energy Conversion and Management (2025) Vol. 327, pp. 119544-119544
Closed 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
Prediction of mechanical properties of recycled aggregate fly ash concrete employing machine learning algorithms
Maedeh Hosseinzadeh, Mehdi Dehestani, Alireza Hosseinzadeh
Journal of Building Engineering (2023) Vol. 76, pp. 107006-107006
Closed Access | Times Cited: 35
Investigation on compressive strength of coral aggregate concrete: Hybrid machine learning models and experimental validation
Zhen Sun, Yalin Li, Yaqi Li, et al.
Journal of Building Engineering (2023) Vol. 82, pp. 108220-108220
Closed Access | Times Cited: 33
Compressive strength prediction of concrete blended with carbon nanotubes using gene expression programming and random forest: hyper-tuning and optimization
Dawei Yang, Ping Xu, Athar Zaman, et al.
Journal of Materials Research and Technology (2023) Vol. 24, pp. 7198-7218
Open Access | Times Cited: 30
Artificial intelligence-based prediction of strengths of slag-ash-based geopolymer concrete using deep neural networks
Solomon Oyebisi, Thamer Alomayri
Construction and Building Materials (2023) Vol. 400, pp. 132606-132606
Open Access | Times Cited: 30
Machine Learning-Based Predictive Model for Tensile and Flexural Strength of 3D-Printed Concrete
Ammar A. Ali, Raja Dilawar Riaz, Umair Jalil Malik, et al.
Materials (2023) Vol. 16, Iss. 11, pp. 4149-4149
Open Access | Times Cited: 28
Development of autogenous shrinkage prediction model of alkali-activated slag-fly ash geopolymer based on machine learning
Jiale Shen, Yue Li, Hui Lin, et al.
Journal of Building Engineering (2023) Vol. 71, pp. 106538-106538
Closed Access | Times Cited: 25
Predicting concrete strength through packing density using machine learning models
Pallapothu Swamy Naga Ratna Giri, Rathish Kumar Pancharathi, Rakesh Janib
Engineering Applications of Artificial Intelligence (2023) Vol. 126, pp. 107177-107177
Closed Access | Times Cited: 25
Prediction of building energy performance using mathematical gene-expression programming for a selected region of dry-summer climate
Majed Alzara, Muhammad Faisal Rehman, Furqan Farooq, et al.
Engineering Applications of Artificial Intelligence (2023) Vol. 126, pp. 106958-106958
Closed Access | Times Cited: 22
A critical review on shear performance of joints in precast Ultra-High-Performance Concrete (UHPC) segmental bridges
Meng Ye, Lifeng Li, Bida Pei, et al.
Engineering Structures (2023) Vol. 301, pp. 117224-117224
Closed Access | Times Cited: 22
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