Shear strengthening of RC beams with fabric-reinforced cementitious matrix: analytical modeling and machine learning approaches
Bibliographic record
Abstract
Fabric-reinforced cementitious matrix (FRCM) systems are increasingly recognized in the construction industry for their notable effectiveness in strengthening reinforced concrete (RC) structures. This study examines the contribution of FRCM systems in the shear strengthening of RC elements using a comprehensive database of 158 shear-strengthened structural elements. The database includes 20 key input parameters related to beam geometry, internal reinforcement, and FRCM systems. A new predictive equation for FRCM shear contribution is developed and validated against five existing analytical models. Additionally, machine learning (ML) algorithms, specifically the extreme gradient boosting (XGBoost) model, are utilized to predict the shear contributions of FRCM systems. The XGBoost model also evaluates the critical input parameters influencing the effectiveness of FRCM systems in improving the shear performance of RC structures. This analysis identifies the fabric depth and the number of fabric layers as the most influential parameters, contributing 28.0% and 23.6%, respectively, to FRCM shear capacity. The properties of both the fabric and the mortar are also identified as crucial factors in improving the performance of the system. This work advances the integration of ML with experimental research to refine design models and provides strategic recommendations for future investigations.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".