Fiber-reinforced recycled aggregate concrete with crumb rubber: A state-of-the-art review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The growing population demands rapid development of infrastructures. However, the construction industry is searching for environmentally sustainable and eco-friendly building materials to fight climate change. Millions of tires are discarded globally, and only certain percentages are recycled. The use of rubber tires as a natural aggregate replacement in concrete has gained popularity among the research community in the past few years, primarily due to its ductility and toughness properties. A significant number of investigations have been reported in the past using recycled coarse aggregates (RCA), crumb rubber (CR), and fibers separately in concrete. The results revealed that the addition of rubber particles along with RCA in concrete reduced the strength. However, the inclusion of fibers in the same mixtures significantly improved the mechanical properties of concrete by acting as a bridge within the concrete matrix for the surrounding cracks. In this review paper, over 220 research articles from the last 30 years reporting the effect of RCA, CR, and fibers on the mechanical and physical properties of rubberized recycled aggregate concrete (RRAC) and fiber-reinforced rubberized recycled aggregate concrete (FRRAC) are summarized. This paper presents in detail the influencing factors that affect the physical and mechanical properties of RRAC and FRRAC. The performance of FRRAC depends on the types of fiber and CR, treatment of CR, RCA sources, and the mix design of concrete. Based on the review, recommendations are provided for optimized FRRAC production. Simplified equations have been proposed to predict the tensile and flexural strength and modulus of elasticity of RRAC and FRRAC. An overview of predicting the mechanical properties of rubberized concrete using different machine-learning algorithms has been presented. Finally, this review paper will help scholars understand the use of RCA and CR in concrete as aggregate replacement materials and create waste material utilization opportunities for the sustainable green construction industry.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it