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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:

Data-driven model for ternary-blend concrete compressive strength prediction using machine learning approach
Babatunde Abiodun Salami, Teslim Olayiwola, Tajudeen A. Oyehan, et al.
Construction and Building Materials (2021) Vol. 301, pp. 124152-124152
Closed Access | Times Cited: 70

Showing 1-25 of 70 citing articles:

Machine learning for structural engineering: A state-of-the-art review
Huu‐Tai Thai
Structures (2022) Vol. 38, pp. 448-491
Closed Access | Times Cited: 414
A novel approach to explain the black-box nature of machine learning in compressive strength predictions of concrete using Shapley additive explanations (SHAP)
I.U. Ekanayake, D.P.P. Meddage, Upaka Rathnayake
Case Studies in Construction Materials (2022) Vol. 16, pp. e01059-e01059
Open Access | Times Cited: 260
Hybrid machine learning model and Shapley additive explanations for compressive strength of sustainable concrete
Yanqi Wu, Yisong Zhou
Construction and Building Materials (2022) Vol. 330, pp. 127298-127298
Closed Access | Times Cited: 139
Revealing the nature of metakaolin-based concrete materials using artificial intelligence techniques
Panagiotis G. Asteris, Paulo B. Lourénço, Panayiotis C. Roussis, et al.
Construction and Building Materials (2022) Vol. 322, pp. 126500-126500
Closed Access | Times Cited: 123
Compressive strength prediction of basalt fiber reinforced concrete via random forest algorithm
Li Hong, Jiajian Lin, Xiaobao Lei, et al.
Materials Today Communications (2022) Vol. 30, pp. 103117-103117
Closed Access | Times Cited: 112
Estimating compressive strength of lightweight foamed concrete using neural, genetic and ensemble machine learning approaches
Babatunde Abiodun Salami, Mudassir Iqbal, Abdulazeez Abdulraheem, et al.
Cement and Concrete Composites (2022) Vol. 133, pp. 104721-104721
Open Access | Times Cited: 99
Evolutionary optimization of machine learning algorithm hyperparameters for strength prediction of high-performance concrete
Sourav Singh, Sanjaya Kumar Patro, Suraj Kumar Parhi
Asian Journal of Civil Engineering (2023) Vol. 24, Iss. 8, pp. 3121-3143
Closed Access | Times Cited: 37
AI-Assisted optimisation of green concrete mixes incorporating recycled concrete aggregates
Peyman Zandifaez, Elyas Asadi Shamsabadi, Ali Akbar Nezhad, et al.
Construction and Building Materials (2023) Vol. 391, pp. 131851-131851
Open Access | Times Cited: 37
Intelligent optimization for modelling superhydrophobic ceramic membrane oil flux and oil-water separation efficiency: Evidence from wastewater treatment and experimental laboratory
Jamilu Usman, Babatunde Abiodun Salami, Afeez Gbadamosi, et al.
Chemosphere (2023) Vol. 331, pp. 138726-138726
Closed Access | Times Cited: 36
Modeling strength characteristics of basalt fiber reinforced concrete using multiple explainable machine learning with a graphical user interface
W.K.V.J.B. Kulasooriya, R.S.S. Ranasinghe, Udara Sachinthana Perera, et al.
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 34
New-generation machine learning models as prediction tools for modeling interfacial tension of hydrogen-brine system
Afeez Gbadamosi, Haruna Adamu, Jamilu Usman, et al.
International Journal of Hydrogen Energy (2023) Vol. 50, pp. 1326-1337
Closed Access | Times Cited: 29
Towards white box modeling of compressive strength of sustainable ternary cement concrete using explainable artificial intelligence (XAI)
Syed Muhammad Ibrahim, Saad Shamim Ansari, Syed Danish Hasan
Applied Soft Computing (2023) Vol. 149, pp. 110997-110997
Closed Access | Times Cited: 26
Compressive strength prediction of ternary-blended concrete using deep neural network with tuned hyperparameters
J. S. Choi, Dongyoun Kim, Minsam Ko, et al.
Journal of Building Engineering (2023) Vol. 75, pp. 107004-107004
Closed Access | Times Cited: 25
Comparative Analysis of Gradient-Boosting Ensembles for Estimation of Compressive Strength of Quaternary Blend Concrete
Ismail B. Mustapha, Muyideen Abdulkareem, Taha M. Jassam, et al.
International Journal of Concrete Structures and Materials (2024) Vol. 18, Iss. 1
Open Access | Times Cited: 12
Predictive modeling of physical and mechanical properties of pervious concrete using XGBoost
Ismail B. Mustapha, Zainab Abdulkareem, Muyideen Abdulkareem, et al.
Neural Computing and Applications (2024) Vol. 36, Iss. 16, pp. 9245-9261
Closed Access | Times Cited: 11
Quantifying compressive strength in limestone powder incorporated concrete with incorporating various machine learning algorithms with SHAP analysis
Mihir Mishra
Asian Journal of Civil Engineering (2024)
Closed Access | Times Cited: 11
Predicting engineering properties of controlled low-strength material made from waste soil using optimized SVR models
Guijie Zhao, Xiaoqiang Pan, Huan Yan, et al.
Case Studies in Construction Materials (2024) Vol. 20, pp. e03325-e03325
Open Access | Times Cited: 9
Predictive modeling for durability characteristics of blended cement concrete utilizing machine learning algorithms
Bo Fu, Hua Lei, Irfan Ullah, et al.
Case Studies in Construction Materials (2025), pp. e04209-e04209
Open Access | Times Cited: 1
Machine Learning as an Innovative Engineering Tool for Controlling Concrete Performance: A Comprehensive Review
Fatemeh Mobasheri, Masoud Hosseinpoor, Ammar Yahia, et al.
Archives of Computational Methods in Engineering (2025)
Closed Access | Times Cited: 1
Concrete-to-concrete interface shear strength prediction based on explainable extreme gradient boosting approach
Jigang Xu, Shi‐Zhi Chen, Weijie Xu, et al.
Construction and Building Materials (2021) Vol. 308, pp. 125088-125088
Closed Access | Times Cited: 47
Estimating Flexural Strength of FRP Reinforced Beam Using Artificial Neural Network and Random Forest Prediction Models
Kaffayatullah Khan, Mudassir Iqbal, Babatunde Abiodun Salami, et al.
Polymers (2022) Vol. 14, Iss. 11, pp. 2270-2270
Open Access | Times Cited: 29
Artificial intelligence techniques in advanced concrete technology: A comprehensive survey on 10 years research trend
Ramin Kazemi
Engineering Reports (2023) Vol. 5, Iss. 9
Open Access | Times Cited: 22
Building energy loads prediction using bayesian-based metaheuristic optimized-explainable tree-based model
Babatunde Abiodun Salami, Sani I. Abba, Adeshina Adewale Adewumi, et al.
Case Studies in Construction Materials (2023) Vol. 19, pp. e02676-e02676
Open Access | Times Cited: 20
Prediction of the compressive strength of normal concrete using ensemble machine learning approach
Sanjog Chhetri Sapkota, Prasenjit Saha, Sourav Das, et al.
Asian Journal of Civil Engineering (2023) Vol. 25, Iss. 1, pp. 583-596
Closed Access | Times Cited: 19
Machine learning-enabled characterization of concrete mechanical strength through correlation of flexural and torsional resonance frequencies
Bai Li, Majid Samavatian, Vahid Samavatian
Physica Scripta (2024) Vol. 99, Iss. 7, pp. 076002-076002
Closed Access | Times Cited: 6
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