Development of Machine Learning Algorithms to Predict the Ultimate Axial Capacity of Fire Damaged Circular Columns Repaired with CFRP Composites
Bibliographic record
Abstract
This paper presents a study that extends the application of carbon fiber reinforced polymer (CFRP) composite confinement technology to strengthen circular concrete columns damaged by fire. This study utilized data from 125 column specimens sourced from the literature. It examined ten parameters: column diameter, height, initial compressive strength of concrete, initial tensile strength of steel, longitudinal reinforcement ratio, fire temperature, exposure time, number of CFRP layers, CFRP thickness, and CFRP tensile modulus, which were used as inputs for the model. The objective was to predict the ultimate axial strength of fire-damaged circular columns repaired with CFRP composites. This study employs both multiple regression analysis and a deep neural network (DNN) to predict the structural behavior of reinforced concrete (RC) columns and accurately forecast their repaired axial capacity. The proposed deep neural network (DNN) model demonstrated a robust agreement with experimental investigations, boasting an overall correlation factor (R) of 0.99852. Deep neural networks outperformed multiple regression analysis in predicting axial strength, with predictions closely matching experimental results from previous studies. The work also presents a parametric study to examine the effect of different input parameters on the axial strength of RC columns. Parametric analysis indicates that the repaired axial strength increases with higher concrete initial compressive strength, greater CFRP thickness and tensile modulus, and more CFRP layers, whereas it decreases with higher fire temperatures, longer exposure durations, and larger column diameters.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".