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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:

Explainable Ensemble Learning Models for the Rheological Properties of Self-Compacting Concrete
Celal Çakıroğlu, Gebrai̇l Bekdaş, Sanghun Kim, et al.
Sustainability (2022) Vol. 14, Iss. 21, pp. 14640-14640
Open Access | Times Cited: 14

Showing 14 citing articles:

Use of Machine Learning Techniques in Soil Classification
Yaren Aydın, Ümit Işıkdağ, Gebrai̇l Bekdaş, et al.
Sustainability (2023) Vol. 15, Iss. 3, pp. 2374-2374
Open Access | Times Cited: 38
Exploring Insights in Biomass and Waste Gasification via Ensemble Machine Learning Models and Interpretability Techniques
Ocident Bongomin, Charles Nzila, Josphat Igadwa Mwasiagi, et al.
International Journal of Energy Research (2024) Vol. 2024, Iss. 1
Open Access | Times Cited: 7
Prediction of self-consolidating concrete properties using XGBoost machine learning algorithm: Rheological properties
Amine el Mahdi Safhi, Hamed Dabiri, Ahmed Soliman, et al.
Powder Technology (2024) Vol. 438, pp. 119623-119623
Open Access | Times Cited: 5
Explainable machine learning-aided efficient prediction model and software tool for bond strength of concrete with corroded reinforcement
Tadesse G. Wakjira, Abdelrahman Abushanab, M. Shahria Alam, et al.
Structures (2023) Vol. 59, pp. 105693-105693
Closed Access | Times Cited: 13
Predictive Modeling of Recycled Aggregate Concrete Beam Shear Strength Using Explainable Ensemble Learning Methods
Celal Çakıroğlu, Gebrai̇l Bekdaş
Sustainability (2023) Vol. 15, Iss. 6, pp. 4957-4957
Open Access | Times Cited: 12
Revolutionizing 3D concrete printing: Leveraging RF model for precise printability and rheological prediction
Songyuan Geng, Mei Liu, Boyuan Cheng, et al.
Journal of Building Engineering (2024) Vol. 88, pp. 109127-109127
Closed Access | Times Cited: 4
Forecasting the rheological state properties of self-compacting concrete mixes using the response surface methodology technique for sustainable structural concreting
Edwin Zumba, Nancy Velasco, Edison Marcelo Melendres Medina, et al.
PLoS ONE (2024) Vol. 19, Iss. 7, pp. e0302202-e0302202
Open Access | Times Cited: 3
Machine learning prediction of concrete frost resistance and optimization design of mix proportions
Jinpeng Dai, Zhijie Zhang, Xiaoyuan Yang, et al.
Journal of Intelligent & Fuzzy Systems (2024), pp. 1-26
Closed Access | Times Cited: 2
Bayesian Optimized LightGBM model for predicting the fundamental vibrational period of masonry infilled RC frames
Taimur Rahman, Pengfei Zheng, Shamima Sultana
Frontiers of Structural and Civil Engineering (2024) Vol. 18, Iss. 7, pp. 1084-1102
Closed Access | Times Cited: 2
The State of Art in Machine Learning Applications in Civil Engineering
Yaren Aydın, Gebrai̇l Bekdaş, Ümit Işıkdağ, et al.
Studies in systems, decision and control (2023), pp. 147-177
Closed Access | Times Cited: 4
Eccentric Compression Behaviors of Self-Compacting Concrete-Filled Thin-Walled Steel Tube Columns
Yunyang Wang, Shengwei Sun, Liqing Zhang, et al.
Materials (2023) Vol. 16, Iss. 18, pp. 6330-6330
Open Access | Times Cited: 3
Ensemble learning for impurity prediction in high-purity indium purified via vertical zone refining
Zhongwen Shang, Mei‐Zhen Wu, Jubo Peng, et al.
Intelligent Systems with Applications (2024) Vol. 22, pp. 200390-200390
Open Access
Review of Recent Advances on AI Applications in Civil Engineering
Yaren Aydın, Gebrai̇l Bekdaş, Sinan Melih Niğdeli
Springer tracts in nature-inspired computing (2024), pp. 107-130
Closed Access
Explainable Machine-Learning Predictions for Peak Ground Acceleration
Rui Sun, Wanwan Qi, Tong Zheng, et al.
Applied Sciences (2023) Vol. 13, Iss. 7, pp. 4530-4530
Open Access | Times Cited: 1
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