Prediction of ultimate strength of FRP-confined predamaged concrete using backward multiple regression motivated soft computing methods
Bibliographic record
Abstract
Confining structurally deficient concrete columns with externally bonded fiber-reinforced polymer (FRP) has been widely accepted as an effective technology for strengthening the ductility and strength of deficient concrete columns. However, prediction models for damaged and afterward repaired concrete based on soft computing methods are not available for the planning and maintenance of concrete structures. Therefore, this paper adopted two soft computing methods – artificial neural network (ANN) and Gaussian process regression (GPR) – to analyze observations obtained from 103 datasets of concentrically loaded FRP-confined predamaged concrete. The models only consider statistically significant variables with the ultimate strength of FRP-confined predamaged concrete. The statistically significant variables based on the multivariate regression analysis are corner radius ratio, FRP thickness, concrete strength, and damage degree. The coefficient of determination of the developed models is greater than 98% and there is a relatively low error between the measured and predicted values. The results of the current study highlight the merit of using soft computing methods in concrete technology given their extraordinary ability to comprehend multidimensional phenomena of concrete structures with ease and high predictivity over the existing empirical models.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".