Design criteria for strut-and-tie modelling in Hong Kong practice
Bibliographic record
Abstract
Strut-and-tie modelling has proved a very useful method in the analysis and design of non-flexural components of reinforced concrete members. Examples of design codes encompassing the strut-and-tie model (STM) provisions include Eurocode 2 (EC2), the fib Model Code 2010 (MC2010), the Canadian Standard (CSA A23.3-04), the American bridge design specifications (AASHTO LRFD 2020), the American Standard (ACI 318-14 and -19) and the Australian Standard (AS 3600:2018). Nevertheless, the application of different assumptions, model types and methodologies means that the strength acceptance criteria for struts, nodes and bearings vary within the literature and design codes. Unifying the STM design criteria is thus encouraged, to facilitate worldwide use of the STM in the design of non-flexural components, particularly for places such as Hong Kong that have not yet developed the localised design criteria for the STM. In this paper, the proposals set out in the literature and the provisions in the above-stated design codes are reviewed and compared with each other. Calibrating with the existing literature and design provisions, unified STM design criteria for struts, nodes and bearings with localisation are proposed for application in Hong Kong. The proposed design criteria are then applied to the STM design of a cantilever deep beam.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".