Bibliographic record
Abstract
콘크리트 깊은 보의 전단강도 산정을 위해 현행 미국콘크리트학회(ACI) 및 캐나다표준규격협회(CSA), 유럽콘크리트위원회(CEB-FIP)의 설계기준은 스트럿-타이 모델을 이용할 것을 제안하고 있지만 설계의 품질이 설계자가 구성한 트러스 모델 적합성에 크게 좌우된다는 특징을 가지고 있다. 따라서 본 논문에서는 내부 트러스 모델에 따른 현행 ACI, CSA 및 CEB-FIP의 콘크리트 깊은 보 설계기준의 타당성을 홍성걸 등에 의해 제안된 콘크리트 소성학에 근거한 전단강도식의 예측치와 비교함으로써 평가한다. 비교 결과 ACI, CSA 및 CEB-FIP의 스트럿-타이 모델에 의해 설계된 깊은 보의 경우 내부 트러스 모델이 전단강도 예측에 중요한 영향을 미치는 것으로 나타났으며 CEB-FIP의 경우 가장 높은 스트럿 강도 예측치를 보였다. 【The current American Concrete Institute(ACI), Canadian Standard Associate(CSA) and CEB-FIP Model Code 2010 provisions on the shear strength of a simply supported deep beam suggest that deep beams should be designed using the strut-and-tie model. Although this is a useful methodology to design members in disturbed regions, the quality of the design is highly dependent on the truss model that designers create. However, Hong et al. derived the shear strength equations of reinforced concrete deep beams. This thesis investigates the validity of the current ACI, CSA and CEB-FIP code provisions on the shear strength of simply supported reinforced concrete deep beams by comparing them with the shear strength equations proposed by Hong et al. The comparison shows that all of these code provisions provide reasonable estimates on the shear strength of concrete deep beam members and the selection of an internal truss model plays an important role on the estimation of shear 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 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".