Behavior of Concrete Deep Beams with High Strength Reinforcement
Bibliographic record
Abstract
The Strut-and-Tie modeling technique is a widely accepted design approach for reinforced concrete deep beams. However, there are significant differences between various design code implementations for this technique with respect to the reinforcement tie influences on the capacity of adjacent concrete struts. Furthermore, each design code specifies different limits on the maximum permitted stress in the ties. Since high-performance reinforcement continues to gain wider acceptance in industry practice, it is necessary to validate existing design approaches, including Strut-and-Tie modeling, for the mechanical properties of these new materials. An experimental campaign was conducted to understand the performance of large-scale reinforced concrete deep beams constructed with high strength longitudinal reinforcement. Test results from six beams are presented, where primary test variables included the shear-span-to-depth aspect ratio and the longitudinal reinforcement ratio. All specimens included a constant quantity of transverse reinforcement. Testing was conducted under a four-point bending configuration, on specimens with cross-section 300 mm wide x 607 mm high. Specimens failed either by shear-compression, or by a flexural mode of longitudinal reinforcement yielding or crushing of the flexural compression zone. The results showed that the member capacity decreased as the shear-span-to-depth ratio increased, and as the longitudinal reinforcement ratio decreased. It was possible to design members to efficiently exploit the high strength reinforcing steel using Strut-and-Tie modeling techniques according to CSA A23.3-04 and ACI 318-05 provisions.
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 | 0.000 | 0.000 |
| 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) | 0.001 | 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".