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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 models for predicting wall thickness loss of water pipes
Ridwan Taiwo, Abdul‐Mugis Yussif, Mohamed El Amine Ben Seghier, et al.
Ain Shams Engineering Journal (2024) Vol. 15, Iss. 4, pp. 102630-102630
Open Access | Times Cited: 9

Showing 9 citing articles:

Generative artificial intelligence in construction: A Delphi approach, framework, and case study
Ridwan Taiwo, Idris Temitope Bello, Sulemana Fatoama Abdulai, et al.
Alexandria Engineering Journal (2025) Vol. 116, pp. 672-698
Closed Access | Times Cited: 2
Machine learning-based Shapley additive explanations approach for corroded pipeline failure mode identification
Mohamed El Amine Ben Seghier, Osama Ahmed Mohamed, Hocine Ouaer
Structures (2024) Vol. 65, pp. 106653-106653
Closed Access | Times Cited: 5
Explainable deep learning models for predicting water pipe failures
Ridwan Taiwo, Tarek Zayed, Beenish Bakhtawar, et al.
Journal of Environmental Management (2025) Vol. 379, pp. 124738-124738
Open Access
A Review of the Application of Machine Learning for Pipeline Integrity Predictive Analysis in Water Distribution Networks
Robert Chen, Qiuping Wang, Ahad Javanmardi
Archives of Computational Methods in Engineering (2025)
Closed Access
Prediction and deployment of compressive strength of high-performance concrete using ensemble learning techniques
Ridwan Taiwo, Abdul‐Mugis Yussif, Adesola Habeeb Adegoke, et al.
Construction and Building Materials (2024) Vol. 451, pp. 138808-138808
Closed Access | Times Cited: 3
Advancing the analysis of water pipe failures: a probabilistic framework for identifying significant factors
Muhammad Muddassir, Tarek Zayed, Ridwan Taiwo, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 2
Interpreting machine learning models based on SHAP values in predicting suspended sediment concentration
Houda Lamane, Latifa Mouhir, Rachid Moussadek, et al.
International Journal of Sediment Research (2024)
Open Access | Times Cited: 1
Development of the new machine-learning approach in pipeline condition assessment prediction and optimizing rehabilitation strategies
Ardalan Sabamehr, Nima Amani, Solomon Boateng, et al.
Journal of Facilities Management (2024)
Closed Access
A comparative study of machine learning methods for identifying the 15 CIE standard skies
Emmanuel Imuetinyan Aghimien, Danny H.W. Li, Ernest K.W. Tsang, et al.
Journal of Building Physics (2024)
Closed Access
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