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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:

Synergistic effects of supplementary cementitious materials and compressive strength prediction of concrete using machine learning algorithms with SHAP and PDP analyses
Rezaul Karim, Md. Hamidul Islam, Shuvo Dip Datta, et al.
Case Studies in Construction Materials (2023) Vol. 20, pp. e02828-e02828
Open Access | Times Cited: 55

Showing 1-25 of 55 citing articles:

Enhancement fracture behavior of sustainable cementitious composites using synergy between fly ash (FA) and nanosilica (NS) in the assessment based on digital image processing procedure
Grzegorz Ludwik Golewski
Theoretical and Applied Fracture Mechanics (2024) Vol. 131, pp. 104442-104442
Closed Access | Times Cited: 55
Artificial intelligence and machine learning applications in the project lifecycle of the construction industry: A comprehensive review
Shuvo Dip Datta, Mobasshira Islam, Md. Habibur Rahman Sobuz, et al.
Heliyon (2024) Vol. 10, Iss. 5, pp. e26888-e26888
Open Access | Times Cited: 50
Hybrid data-driven approaches to predicting the compressive strength of ultra-high-performance concrete using SHAP and PDP analyses
Abul Kashem, Rezaul Karim, Somir Chandra Malo, et al.
Case Studies in Construction Materials (2024) Vol. 20, pp. e02991-e02991
Open Access | Times Cited: 45
Compressive strength prediction of sustainable concrete incorporating rice husk ash (RHA) using hybrid machine learning algorithms and parametric analyses
Abul Kashem, Rezaul Karim, Pobithra Das, et al.
Case Studies in Construction Materials (2024) Vol. 20, pp. e03030-e03030
Open Access | Times Cited: 31
Machine learning and interactive GUI for concrete compressive strength prediction
Mohamed Kamel Elshaarawy, Mostafa M. Alsaadawi, Abdelrahman Kamal Hamed
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 31
Performance evaluation of concrete made with plastic waste using multi-expression programming
Usama Asif, Muhammad Faisal Javed, Mana Alyami, et al.
Materials Today Communications (2024) Vol. 39, pp. 108789-108789
Closed Access | Times Cited: 19
Improved forecasting of the compressive strength of ultra‐high‐performance concrete (UHPC) via the CatBoost model optimized with different algorithms
Metin Katlav, Faruk Ergen
Structural Concrete (2024)
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
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
Ultra-light foamed concrete mechanical properties and thermal insulation perspective: A comprehensive review
Abdeliazim Mustafa Mohamed, Bassam A. Tayeh, Samadar S. Majeed, et al.
Journal of CO2 Utilization (2024) Vol. 83, pp. 102827-102827
Open Access | Times Cited: 9
Evaluation of textile effluent treatment plant sludge as supplementary cementitious material in concrete using experimental and machine learning approaches
Md Mottakin, Shuvo Dip Datta, Md. Mehrab Hossain, et al.
Journal of Building Engineering (2024) Vol. 96, pp. 110627-110627
Closed Access | Times Cited: 8
Short- and long-term experimental performance of concrete with copper slag: Mechanical and physical properties assessment
Yimmy Fernando Silva Urrego, C. Burbano-Garcia, Gerardo Araya-Letelier, et al.
Case Studies in Construction Materials (2024) Vol. 20, pp. e03302-e03302
Open Access | Times Cited: 6
Indirect prediction of graphene nanoplatelets-reinforced cementitious composites compressive strength by using machine learning approaches
Muhammad Fawad, Hisham Alabduljabbar, Furqan Farooq, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 6
Innovative hybrid machine learning models for estimating the compressive strength of copper mine tailings concrete
Mana Alyami, Kennedy C. Onyelowe, Ali H. AlAteah, et al.
Case Studies in Construction Materials (2024) Vol. 21, pp. e03869-e03869
Open Access | Times Cited: 6
Assessing the Influence of Banana Leaf Ash as Pozzolanic Material for the Production of Green Concrete: A Mechanical and Microstructural Evaluation
Md Hamidul Islam, David W. Law, Chamila Gunasekara, et al.
Materials (2024) Vol. 17, Iss. 3, pp. 720-720
Open Access | Times Cited: 5
Fresh, hardened, durability and microstructure properties of seawater concrete: A systematic review
Abdeliazim Mustafa Mohamed, Bassam A. Tayeh, Samadar S. Majeed, et al.
Journal of CO2 Utilization (2024) Vol. 83, pp. 102815-102815
Open Access | Times Cited: 5
Predicting compressive strength of hollow concrete prisms using machine learning techniques and explainable artificial intelligence (XAI)
Waleed Bin Inqiad, Elena Valentina Dumitrascu, Robert Alexandru Dobre, et al.
Heliyon (2024) Vol. 10, Iss. 17, pp. e36841-e36841
Open Access | Times Cited: 5
Tree-based machine learning models for predicting the bond strength in reinforced recycled aggregate concrete
Alireza Mahmoudian, Maryam Bypour, Denise‐Penelope N. Kontoni
Asian Journal of Civil Engineering (2024)
Closed Access | Times Cited: 5
Artificial intelligence-enhanced non-destructive defect detection for civil infrastructure
Yong‐Wei Zhang, Cheuk Lun Chow, Denvid Lau
Automation in Construction (2025) Vol. 171, pp. 105996-105996
Open Access
Optimizing high-strength concrete compressive strength with explainable machine learning
Sanjog Chhetri Sapkota, Christina Panagiotakopoulou, Dipak Dahal, et al.
Multiscale and Multidisciplinary Modeling Experiments and Design (2025) Vol. 8, Iss. 3
Open Access
Machine learning study on magnetic structure of rare earth based magnetic materials
Dan Liu, J. J. Song, Zhixin Liu, et al.
Materials & Design (2025) Vol. 251, pp. 113710-113710
Open Access
Multi-objective Optimization Framework for Nitrogen-containing Compound Generation in Nitrogen-enriched Pyrolysis: Integrating Fransfer Learning and Experimental Validation
Hui Wang, Dongmei Bi, Qingqing Qian, et al.
Journal of Analytical and Applied Pyrolysis (2025), pp. 107070-107070
Closed Access
Novel approaches in prediction of tensile strain capacity of engineered cementitious composites using interpretable approaches
Turki S. Alahmari, Furqan Farooq
REVIEWS ON ADVANCED MATERIALS SCIENCE (2025) Vol. 64, Iss. 1
Open Access
Experimental assessment and hybrid machine learning-based feature importance analysis with the optimization of compressive strength of waste glass powder-modified concrete
Turki S. Alahmari, Md. Kawsarul Islam Kabbo, Md. Habibur Rahman Sobuz, et al.
Materials Today Communications (2025), pp. 112081-112081
Closed Access
Analyzing the compressive strength, eco-strength, and cost–strength ratio of agro-waste-derived concrete using advanced machine learning methods
Muhammad Nasir Amin, Bawar Iftikhar, Kaffayatullah Khan, et al.
REVIEWS ON ADVANCED MATERIALS SCIENCE (2025) Vol. 64, Iss. 1
Open Access
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