Data‐driven assessment and design of axially loaded <scp>FRP</scp> ‐reinforced concrete columns
Bibliographic record
Abstract
Abstract Fiber‐reinforced polymer (FRP) bars are increasingly used in construction due to their high strength‐to‐weight ratio and resistance to corrosion. Despite these advantages, existing design codes for FRP‐reinforced concrete columns (FRP‐RCC) lack comprehensive equations that adequately account for factors such as slenderness, eccentricity, and the contribution of FRP bars to axial capacity. This study addresses these gaps by proposing an artificial neural network (ANN) model to accurately predict the axial capacity of short, slender, concentric, and eccentric FRP‐RCCs. A comprehensive database of 490 column samples from the literature was utilized to analyze the factors influencing FRP‐RCC behavior, develop the ANN model, and validate its performance. The database also enabled a detailed evaluation of current design codes, existing ANN models, and other design equations, comparing their predictive capabilities to the proposed ANN. The results demonstrate the effectiveness of the proposed ANN model, achieving an R 2 value of 0.93 during training and testing and 0.97 during verification with an independent dataset. These findings underscore the model's robustness and practical applicability in engineering design. Furthermore, comparative analysis revealed that the proposed ANN consistently outperforms existing design equations and available ANN models, highlighting its superior predictive accuracy and potential as a reliable tool for designing FRP‐reinforced concrete structures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".