A Review of Strength and Durability Testing and Artificial Intelligence Prediction Methods for Various Fiber‐Reinforced Concretes.
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| Title: | A Review of Strength and Durability Testing and Artificial Intelligence Prediction Methods for Various Fiber‐Reinforced Concretes. |
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| Authors: | N. S., Ninu Praseetha1 (AUTHOR) ninupraseetha@gmail.com, Kaythry, P.1 (AUTHOR), Sangeetha, P.1 (AUTHOR) |
| Source: | Structural Design of Tall & Special Buildings. May2025, Vol. 34 Issue 7, p1-29. 29p. |
| Subjects: | Concrete testing, Artificial intelligence, Deep learning, Machine learning, Strength of materials, Synthetic fibers |
| Abstract: | Concrete is a very adaptable building material made of cement paste and aggregates. Concrete is vital in the construction sector because of its strength, durability, affordability, and versatility. Tensile strength and material durability are two important considerations for engineers and builders when constructing buildings. Fiber‐reinforced concrete (FRC) is a well‐known type of concrete that uses synthetic and natural fibers to strengthen its mechanical properties. The conventional approaches for concrete testing are also covered in this study. These methods frequently entail sophisticated laboratory apparatus and call for specific knowledge. With the development of technology, new concrete strength prediction techniques have emerged to provide more accurate and efficient ways. This paper reviews the strength and durability testing for various FRC to assess its mechanical properties like compressive, flexural, and tensile strength, as well as its resistance to environmental factors like freeze–thaw cycles and chemical attack. It also explores the application of artificial intelligence (AI) to predict the performance and behavior of FRC in various applications, offering advantages over traditional methods due to their ability to handle complex data and relationships. In AI, machine learning (ML) and deep learning (DL) models, which have major advantages for the construction industry, are also analyzed. [ABSTRACT FROM AUTHOR] |
| Copyright of Structural Design of Tall & Special Buildings is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Abstract: | Concrete is a very adaptable building material made of cement paste and aggregates. Concrete is vital in the construction sector because of its strength, durability, affordability, and versatility. Tensile strength and material durability are two important considerations for engineers and builders when constructing buildings. Fiber‐reinforced concrete (FRC) is a well‐known type of concrete that uses synthetic and natural fibers to strengthen its mechanical properties. The conventional approaches for concrete testing are also covered in this study. These methods frequently entail sophisticated laboratory apparatus and call for specific knowledge. With the development of technology, new concrete strength prediction techniques have emerged to provide more accurate and efficient ways. This paper reviews the strength and durability testing for various FRC to assess its mechanical properties like compressive, flexural, and tensile strength, as well as its resistance to environmental factors like freeze–thaw cycles and chemical attack. It also explores the application of artificial intelligence (AI) to predict the performance and behavior of FRC in various applications, offering advantages over traditional methods due to their ability to handle complex data and relationships. In AI, machine learning (ML) and deep learning (DL) models, which have major advantages for the construction industry, are also analyzed. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 15417794 |
| DOI: | 10.1002/tal.70036 |
