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:
An interpretable probabilistic machine learning model for forecasting compressive strength of oil palm shell-based lightweight aggregate concrete containing fly ash or silica fume
Yang‐Kook Sun, Han‐Seung Lee
Construction and Building Materials (2024) Vol. 426, pp. 136176-136176
Closed Access | Times Cited: 6
Yang‐Kook Sun, Han‐Seung Lee
Construction and Building Materials (2024) Vol. 426, pp. 136176-136176
Closed Access | Times Cited: 6
Showing 6 citing articles:
Predicting Compressive Strength of Oil Well Cement Slurries: Novel Moduli‐Based Analysis of Chemical Composition at Different Temperature Condition
Mohammed A. Jamal, Ahmed Salih Mohammed, Jagar A. Ali
The Structural Design of Tall and Special Buildings (2025) Vol. 34, Iss. 2
Closed Access
Mohammed A. Jamal, Ahmed Salih Mohammed, Jagar A. Ali
The Structural Design of Tall and Special Buildings (2025) Vol. 34, Iss. 2
Closed 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
Sanjog Chhetri Sapkota, Christina Panagiotakopoulou, Dipak Dahal, et al.
Multiscale and Multidisciplinary Modeling Experiments and Design (2025) Vol. 8, Iss. 3
Open Access
Advanced machine learning techniques for predicting concrete mechanical properties: a comprehensive review of models and methodologies
Fangyuan Li, Md. Sohel Rana, Muhammad Ahmed Qurashi
Multiscale and Multidisciplinary Modeling Experiments and Design (2024) Vol. 8, Iss. 1
Closed Access | Times Cited: 2
Fangyuan Li, Md. Sohel Rana, Muhammad Ahmed Qurashi
Multiscale and Multidisciplinary Modeling Experiments and Design (2024) Vol. 8, Iss. 1
Closed Access | Times Cited: 2
Estimation of compressive strength of concrete with manufactured sand and natural sand using interpretable artificial intelligence
Xiaodong Liu, Shengqi Mei, Xingju Wang, et al.
Case Studies in Construction Materials (2024) Vol. 21, pp. e03840-e03840
Open Access | Times Cited: 1
Xiaodong Liu, Shengqi Mei, Xingju Wang, et al.
Case Studies in Construction Materials (2024) Vol. 21, pp. e03840-e03840
Open Access | Times Cited: 1
EVALUATING THE MECHANICAL AND DURABILITY PROPERTIES OF SUSTAINABLE LIGHTWEIGHT CONCRETE INCORPORATING THE VARIOUS PROPORTIONS OF WASTE PUMICE AGGREGATE
Hafiz Muhammad Shahzad Aslam, Atteq ur Rehman, Kennedy C. Onyelowe, et al.
Results in Engineering (2024), pp. 103496-103496
Open Access | Times Cited: 1
Hafiz Muhammad Shahzad Aslam, Atteq ur Rehman, Kennedy C. Onyelowe, et al.
Results in Engineering (2024), pp. 103496-103496
Open Access | Times Cited: 1
Interpretable machine‐learning models for predicting creep recovery of concrete
Shengqi Mei, Xiaodong Liu, Xingju Wang, et al.
Structural Concrete (2024)
Closed Access
Shengqi Mei, Xiaodong Liu, Xingju Wang, et al.
Structural Concrete (2024)
Closed Access
