Shear Strengthening of RC Beams Incorporating Post-Tensioned Bars and Engineered Cementitious Composite Reinforced with Palm Fronds
Bibliographic record
Abstract
This paper investigates, experimentally and numerically, the shear strengthening of Normal Concrete (NC) beams using post-tensioning steel bars and Engineered Cementitious Composite (ECC) reinforced with chemically cured Palm Fronds (PFs). The benefits of strain-hardening ECC and the tensile strength of PFs cured with 6% wt Alkali NaOH solution beside post-tensioned bars have been employed herein. Seven full-scale Reinforced Concrete (RC) beams were fabricated and experimented with under three-point loading until failure. The test parameters include the strengthening technique, type, and configuration of the material used for strengthening. The strengthening process has been implemented through two techniques: Externally Bonded Reinforcement (EBR) and Near-Surface Mounted (NSM) Reinforcement. The strengthening materials have been configured and placed in horizontal, vertical, and inclined positions. The effectiveness of the strengthening methods has been evaluated by examining their cracking propagations, load-deflection responses, collapse modes, elastic stiffness, and absorbed energy. It was found that the proposed strengthening systems could significantly control the crack pattern and failure mode, and they could enhance the ultimate load amplitude up to 37% and 50% for NSM ECC with PFs and EBR post-tensioning steel bars, respectively. Nonlinear three-dimensional finite element models of the tested beams were developed and validated with the test data, where it was found that finite element models predict the structural performance of tested beams with a maximum error of only 2%.
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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 | 0.000 | 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) | 0.000 | 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".