Strengthening Beams using FRCM Machine Learning Approach and Numerical Models
Bibliographic record
Abstract
The use of Fiber Reinforced Cement Matrix (FRCM) over traditional Fiber Reinforced Polymers (FRP) systems has gained attention due to FRCM's superior performance in various conditions.Despite the existing studies, predicting failure and post-peak behavior of FRCM has remained a challenge.This research leverages Machine Learning (ML) techniques to evaluate the capacity of FRCM-strengthened beams.This innovative approach addresses the limitations associated with both experimental and numerical models by providing a comprehensive model capable of predicting the response of FRCM-strengthened beams.The model considers various factors, including mechanical and geometric properties of the beams, steel reinforcement, and characteristics of the FRCM layers such as number, type, compressive strength of mortar, and thickness.A novel approach to employ ML is adopted to utilize three separate ML models to assess the beam capacity in pre-peak, failure, and post-peak stages.Following the ML model validation, verified Finite Element (FE) models are used to ensure the proposed model's robustness in handling unseen data.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".