Numerical experimental and theoretical investigation of reinforced concrete elements with rectangular spiral rebar for multi-behavior analysis
Bibliographic record
Abstract
Rectangular Spiral Rebar (RSR) is an innovative alternative to conventional ties, offering simplified construction and enhanced performance in reinforced concrete (RC) members. Despite its advantages, a comprehensive evaluation of RSR under multi-behavioral loading conditions (compression, shear, and seismic) remains unexplored. This study integrates numerical, experimental, and theoretical approaches. Numerical models were developed using Abaqus, SAP2000, and VecTor2 to assess RSR performance under static (compression/shear), lateral quasi-static, and seismic loads (five intensity levels). Experimentally, 14 RC short columns with RSR were tested under monotonic compression, varying cross-sectional geometries (rectangular/square) and reinforcement configurations. The Mander confined concrete model was extended to incorporate RSR, refining predictions for longitudinal rebar, cover concrete, and core concrete behavior. RSR increased compressive strength by 8.4% compared to conventional ties, with more uniform stress distribution. Nonlinear finite element analysis (VecTor2) accurately predicted crack patterns, though computational models and standards (e.g., SMCFT, MPLANE, CEB-FIP, CSA, AASHTO) overestimated shear capacity. RSR confinement produced blunter crack patterns. RSR-confined frames exhibited 8% lower drifts under high-intensity ground motions (PGA = 0.52 g) and required 12% less transverse reinforcement. The proposed theoretical method showed high accuracy (average experimental-to-theoretical capacity ratio: 1.03 for RSR specimens). This study bridges critical gaps in RSR research, demonstrating its multi-behavior efficacy in improving strength, constructability, and seismic resilience. The validated analytical model provide a foundation for code adoption and practical applications.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".