A review of high-performance fiber concrete for airport pavements
Bibliographic record
Abstract
This study investigates the significance and prospects of utilizing high-performance fiber-reinforced concrete in airport pavements. With the rapid development of the aviation industry, higher performance demands are placed on airport pavements. Traditional airport pavements are predominantly made from ordinary cement concrete, but this material exhibits significant issues of brittleness and low durability when subjected to heavy aircraft and harsh weather conditions. Consequently, fiber-reinforced concrete, known for its exceptional strength and toughness, has garnered considerable attention. This paper discusses in detail the application of various types of fiber materials in strengthening concrete, including steel and basalt fibers. These fibers, based on their chemical and physical properties, play distinct roles in the concrete, thereby enhancing its overall performance. For instance, steel fibers possess a high modulus of elasticity and tensile strength, but are prone to corrosion in acidic environments, while carbon fibers are renowned for their light weight, high strength, and stability. Additionally, the paper emphasizes the importance of mix design methods, as the incorporation of fibers alters the composition and structure of the concrete. Appropriate concrete mix design methods need to be selected. Although fiber-reinforced concrete is widely used in the field of construction engineering, its research and application in airport pavements are not yet sufficiently extensive. Studies should go beyond laboratory tests to explore the evolution of concrete performance in actual usage environments. Consideration should also be given to the unique usage scenarios and stress characteristics of airport pavements to select suitable types of fibers. Moreover, research on the dynamic mechanical properties of fiber-reinforced concrete is a key aspect in enhancing the performance and service life of airport pavements.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".