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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 concrete strengths enabled by missing data imputation and interpretable machine learning
Gideon A. Lyngdoh, Mohd Zaki, N. M. Anoop Krishnan, et al.
Cement and Concrete Composites (2022) Vol. 128, pp. 104414-104414
Open Access | Times Cited: 136

Showing 1-25 of 136 citing articles:

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
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: 104
Prediction of compressive strength of rice husk ash concrete based on stacking ensemble learning model
Qingfu Li, Zongming Song
Journal of Cleaner Production (2022) Vol. 382, pp. 135279-135279
Closed Access | Times Cited: 92
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: 87
Estimation of strength, rheological parameters, and impact of raw constituents of alkali-activated mortar using machine learning and SHapely Additive exPlanations (SHAP)
Sohaib Nazar, Jian Yang, Xing-er Wang, et al.
Construction and Building Materials (2023) Vol. 377, pp. 131014-131014
Closed Access | Times Cited: 54
Multi-objective optimization of fly ash-slag based geopolymer considering strength, cost and CO2 emission: A new framework based on tree-based ensemble models and NSGA-II
Yimiao Huang, Zehui Huo, Guowei Ma, et al.
Journal of Building Engineering (2023) Vol. 68, pp. 106070-106070
Closed Access | Times Cited: 53
Interpretable machine learning for predicting the strength of 3D printed fiber-reinforced concrete (3DP-FRC)
Md Nasir Uddin, Junhong Ye, Bo-Yu Deng, et al.
Journal of Building Engineering (2023) Vol. 72, pp. 106648-106648
Closed Access | Times Cited: 49
Metaheuristic optimization based- ensemble learners for the carbonation assessment of recycled aggregate concrete
Emadaldin Mohammadi Golafshani, Ali Behnood, Taehwan Kim, et al.
Applied Soft Computing (2024) Vol. 159, pp. 111661-111661
Open Access | Times Cited: 19
New Opportunity: Materials Genome Strategy for Engineered Cementitious Composites (ECC) Design
Wenguang Chen, Long Liang, Fangming Jiang, et al.
Cement and Concrete Composites (2025) Vol. 159, pp. 106009-106009
Closed Access | Times Cited: 3
A new method to evaluate features importance in machine-learning based prediction of concrete compressive strength
Yuanhao Dong, Jinhui Tang, Xiaohang Xu, et al.
Journal of Building Engineering (2025), pp. 111874-111874
Closed Access | Times Cited: 2
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 | Times Cited: 2
Advancing Sustainable Concrete Using Biochar: Experimental and Modelling Study for Mechanical Strength Evaluation
Waqas Ahmad, Venkata Satya Sai Chandra Sekhar Veeraghantla, A. R. Byrne
Sustainability (2025) Vol. 17, Iss. 6, pp. 2516-2516
Open Access | Times Cited: 2
Compressive strength prediction of one-part alkali activated material enabled by interpretable machine learning
Syed Farasat Ali Shah, Bing Chen, Muhammad Zahid, et al.
Construction and Building Materials (2022) Vol. 360, pp. 129534-129534
Closed Access | Times Cited: 51
A Comparison of Machine Learning Tools That Model the Splitting Tensile Strength of Self-Compacting Recycled Aggregate Concrete
Jesús de‐Prado‐Gil, Covadonga Palencia, P. Jagadesh, et al.
Materials (2022) Vol. 15, Iss. 12, pp. 4164-4164
Open Access | Times Cited: 47
Assessing the compressive strength of self-compacting concrete with recycled aggregates from mix ratio using machine learning approach
P. Jagadesh, Jesús de Prado-Gil, Neemias Silva-Monteiro, et al.
Journal of Materials Research and Technology (2023) Vol. 24, pp. 1483-1498
Open Access | Times Cited: 39
Compressive strength prediction of high-strength concrete using hybrid machine learning approaches by incorporating SHAP analysis
Abul Kashem, Pobithra Das
Asian Journal of Civil Engineering (2023) Vol. 24, Iss. 8, pp. 3243-3263
Closed Access | Times Cited: 39
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
Prediction of chloride ion concentration distribution in basalt-polypropylene fiber reinforced concrete based on optimized machine learning algorithm
Zhen Sun, Yalin Li, Yaqi Li, et al.
Materials Today Communications (2023) Vol. 36, pp. 106565-106565
Closed Access | Times Cited: 32
Predicting carbonation depth of concrete using a hybrid ensemble model
Zehui Huo, Ling Wang, Yimiao Huang
Journal of Building Engineering (2023) Vol. 76, pp. 107320-107320
Closed Access | Times Cited: 30
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
Machine learning guided design of microencapsulated phase change materials-incorporated concretes for enhanced freeze-thaw durability
Hewenxuan Li, Gideon A. Lyngdoh, N. M. Anoop Krishnan, et al.
Cement and Concrete Composites (2023) Vol. 140, pp. 105090-105090
Open Access | Times Cited: 24
Optimizing process parameters of in-situ laser assisted cutting of glass–ceramic by applying hybrid machine learning models
Jiachen Wei, Wenbin He, Chuangting Lin, et al.
Advanced Engineering Informatics (2024) Vol. 62, pp. 102590-102590
Closed Access | Times Cited: 17
Prediction of compressive strength and tensile strain of engineered cementitious composite using machine learning
Md Nasir Uddin, N. Shanmugasundaram, S. Praveenkumar, et al.
International Journal of Mechanics and Materials in Design (2024) Vol. 20, Iss. 4, pp. 671-716
Closed Access | Times Cited: 15
A comprehensive comparison of various machine learning algorithms used for predicting the splitting tensile strength of steel fiber-reinforced concrete
Seyed Soroush Pakzad, Mansour Ghalehnovi, Atiye Ganjifar
Case Studies in Construction Materials (2024) Vol. 20, pp. e03092-e03092
Open Access | Times Cited: 12
Machine learning based prediction models for spilt tensile strength of fiber reinforced recycled aggregate concrete
Mohammed Alarfaj, Hisham Jahangir Qureshi, Muhammad Zubair Shahab, et al.
Case Studies in Construction Materials (2024) Vol. 20, pp. e02836-e02836
Open Access | Times Cited: 10
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