Effect of Optical Fibers on Selected Characteristics of Concrete
Bibliographic record
Abstract
The incorporation of optical fibers into concrete has the potential to greatly reduce power consumption for lighting in buildings, leading to increased energy efficiency.The goal of this study is to examine the properties of concrete that has optical fibers infused into it in order to establish the minimum optical fiber content required to sustain both the structural integrity and light transmission properties of the concrete.The investigation employs four distinct samples, subjecting them to assessments of compressive strength, flexural strength, and light transmission subsequent to conducting flow table and air content tests.The sample series encompasses varying proportions of optical fibers, specifically 0%, 3%, 5%, and 6% by volume.The outcomes reveal a positive correlation between light transmission and the concentration of optical fibers.Nevertheless, a notable reduction in mechanical strength becomes apparent beyond an optical fiber content of 3%.This study aims to provide information on the complex interactions between optical fiber content and concrete's structural stability and light transmission.The results not only highlight the potential for optimizing light-related energy consumption in buildings through optical fiber integration, but they also highlight how crucial it is to maintain a balance between improved illumination and structural strength in the quest for sustainable and energy-efficient building materials.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".