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Performance Evaluation of Light Transmitting Concrete Made with Plastic Optical Fibers

2024· book-chapter· en· W4392342665 on OpenAlexaff
Shishir Kumar Sikder Amit, Sohel Rana, Adan Bishar Hussein, Md. Mahfuzul Islam

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldEngineering
TopicAdvanced Fiber Optic Sensors
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsMaterials scienceComposite materialOptical fiberComputer scienceTelecommunications

Abstract

fetched live from OpenAlex

Light transmitting concrete (LTC) is a concrete made with light optical elements (e.g., plastic optical fibers) having the light transmitting property. In addition to its use as aesthetic purpose, LTC enhances the utilization of natural resource by the projection of light through the concrete. Thus, this translucent concrete can be used in structures to make energy efficient and environment friendly structures through the minimization of artificial energy usage. This chapter deals with the performance evaluation of light transmitting concrete (LTC) made with plastic optical fibers (POFs). For this purpose, compressive strength and light transmitting properties of LTC have been investigated and compared with conventional concrete. Experimental results show that the compressive strength at 7 days, 14 days, and 28 days have been increased by 3.47-12.23%, 4.82-14.85%, and 4.07-9.78%, respectively for the LTC made with various percentage of POFs. From the simple light transmission test, it has been found that the LTC made with 0.75 mm, 1 mm, and double layer 0.75 mm POFs can transmit 8%, 9.44%, and 14.8% light, respectively. Results of the present study imply that light transmitting concrete made with POFs can be used for aesthetic and energy saving purpose without compromising the strength.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.014
GPT teacher head0.218
Teacher spread0.203 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2024
Admission routes1
Has abstractyes

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