Artificial intelligence-based estimation of ultra-high-strength concrete's flexural property
Article Properties
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LanguageEnglish
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DOI (url)
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Publication Date2022/12/01
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Indian UGC (journal)
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Refrences71
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Citations10
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Qichen Wang
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Abasal Hussain
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Muhammad Usman Farooqi
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Ahmed Farouk Deifalla
Cite
Wang, Qichen, et al. “Artificial Intelligence-Based Estimation of Ultra-High-Strength concrete’s Flexural Property”. Case Studies in Construction Materials, vol. 17, 2022, p. e01243, https://doi.org/10.1016/j.cscm.2022.e01243.
Wang, Q., Hussain, A., Farooqi, M. U., & Deifalla, A. F. (2022). Artificial intelligence-based estimation of ultra-high-strength concrete’s flexural property. Case Studies in Construction Materials, 17, e01243. https://doi.org/10.1016/j.cscm.2022.e01243
Wang, Qichen, Abasal Hussain, Muhammad Usman Farooqi, and Ahmed Farouk Deifalla. “Artificial Intelligence-Based Estimation of Ultra-High-Strength concrete’s Flexural Property”. Case Studies in Construction Materials 17 (2022): e01243. https://doi.org/10.1016/j.cscm.2022.e01243.
1.
Wang Q, Hussain A, Farooqi MU, Deifalla AF. Artificial intelligence-based estimation of ultra-high-strength concrete’s flexural property. Case Studies in Construction Materials. 2022;17:e01243.
Journal Categories
Science
Chemistry
Technology
Building construction
Architectural engineering
Structural engineering of buildings
Technology
Electrical engineering
Electronics
Nuclear engineering
Materials of engineering and construction
Mechanics of materials
Technology
Engineering (General)
Civil engineering (General)
Refrences
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
Hybrid fiber concrete with different basalt fiber length and content | Structural Concrete |
| 58 | 2022 |
Using Artificial Intelligence Techniques to Predict Punching Shear Capacity of Lightweight Concrete Slabs | Materials |
| 22 | 2022 |
Predicting the Mechanical Properties of RCA-Based Concrete Using Supervised Machine Learning Algorithms | Materials |
| 49 | 2022 |
Machine Learning Prediction Models to Evaluate the Strength of Recycled Aggregate Concrete | Materials |
| 45 | 2022 |
Evaluation of the Strength of Slab-Column Connections with FRPs Using Machine Learning Algorithms | Polymers |
| 27 | 2022 |
Refrences Analysis
The category
Technology: Electrical engineering. Electronics. Nuclear engineering: Materials of engineering and construction. Mechanics of materials 48
is the most frequently represented among the references in this article. It primarily includes studies from Construction and Building Materials and Materials. The chart below illustrates the number of referenced publications per year.
Refrences used by this article by year
Citations
Citations Analysis
The category
Science: Chemistry 6
is the most commonly referenced area in studies that cite this article. The first research to cite this article was titled An overview of progressive advancement in ultra-high performance concrete with steel fibers and was published in 2022. The most recent citation comes from a 2024 study titled Мethods of increasing the input capacity of industrial housing construction enterprises. This article reached its peak citation in 2023, with 7 citations. It has been cited in 8 different journals, 37% of which are open access. Among related journals, the Frontiers in Materials cited this research the most, with 3 citations. The chart below illustrates the annual citation trends for this article.