Compressive strength of concrete with recycled aggregate; a machine learning-based evaluation
Article Properties
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LanguageEnglish
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DOI (url)
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Publication Date2022/03/01
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Journal
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Indian UGC (journal)
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Refrences61
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Citations15
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Hamed Dabiri ORCID (unauthenticated)
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Mahdi Kioumarsi ORCID (unauthenticated)
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Ali Kheyroddin ORCID (unauthenticated)
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Amirreza Kandiri ORCID (unauthenticated)
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Farid Sartipi
Cite
Dabiri, Hamed, et al. “Compressive Strength of Concrete With Recycled Aggregate; A Machine Learning-Based Evaluation”. Cleaner Materials, vol. 3, 2022, p. 100044, https://doi.org/10.1016/j.clema.2022.100044.
Dabiri, H., Kioumarsi, M., Kheyroddin, A., Kandiri, A., & Sartipi, F. (2022). Compressive strength of concrete with recycled aggregate; a machine learning-based evaluation. Cleaner Materials, 3, 100044. https://doi.org/10.1016/j.clema.2022.100044
Dabiri H, Kioumarsi M, Kheyroddin A, Kandiri A, Sartipi F. Compressive strength of concrete with recycled aggregate; a machine learning-based evaluation. Cleaner Materials. 2022;3:100044.
Refrences
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
| A novel artificial intelligence technique to predict compressive strength of recycled aggregate concrete using ICA-XGBoost model | Engineering with Computers |
| 164 | 2021 |
| Experimental and thermodynamic study of alkali-activated waste glass and calcium sulfoaluminate cement blends: shrinkage, efflorescence potential, and phase assemblages | Journal of Materials in Civil Engineering |
| 2021 | |
| Machine Learning Algorithms in Civil Structural Health Monitoring: A Systematic Review | Archives of Computational Methods in Engineering |
| 162 | 2021 |
| Engineering performance of high-content MgO-Alkali-activated slag mortar incorporating fine recycled concrete aggregate and fly ash | Journal of Material Cycles and Waste Management |
| 9 | 2021 |
Predicting Compressive Strength of Concrete Containing Recycled Aggregate Using Modified ANN with Different Optimization Algorithms | Applied Sciences |
| 41 | 2021 |
Refrences Analysis
The category
Technology: Engineering (General). Civil engineering (General) 47
is the most frequently represented among the references in this article. It primarily includes studies from Construction and Building Materials The chart below illustrates the number of referenced publications per year.
Refrences used by this article by year
Citations
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
| A comparative study of ensemble machine learning models for compressive strength prediction in recycled aggregate concrete and parametric analysis | Multiscale and Multidisciplinary Modeling, Experiments and Design |
| 2024 | |
| Predicting the mechanical properties of plastic concrete: An optimization method by using genetic programming and ensemble learners | Case Studies in Construction Materials |
| 2024 | |
A Meta-Analysis of the Effect of Moisture Content of Recycled Concrete Aggregate on the Compressive Strength of Concrete | Applied Sciences |
| 2024 | |
Artificial intelligence techniques in advanced concrete technology: A comprehensive survey on 10 years research trend | Engineering Reports |
| 4 | 2023 |
Automated analysis and design for recycled aggregate reinforced concrete beams | Structural Concrete |
| 2023 |
Citations Analysis
The category
Technology: Engineering (General). Civil engineering (General) 9
is the most commonly referenced area in studies that cite this article. The first research to cite this article was titled Experimental and theoretical study on bonding performance of FRP bars-Recycled aggregate concrete and was published in 2022. The most recent citation comes from a 2024 study titled A Meta-Analysis of the Effect of Moisture Content of Recycled Concrete Aggregate on the Compressive Strength of Concrete. This article reached its peak citation in 2022, with 8 citations. It has been cited in 12 different journals, 33% of which are open access. Among related journals, the Buildings cited this research the most, with 3 citations. The chart below illustrates the annual citation trends for this article.