Data-driven framework with graphical user interface for predicting flexural behavior of FRCM strengthened RC beams
Bibliographic record
Abstract
In this study, the flexural capacity of reinforced concrete (RC) beams strengthened with fiber-reinforced cementitious matrices (FRCM) was computed using machine learning (ML) algorithms including: (i) adaptive neuro-fuzzy inference system (ANFIS), (ii) artificial neural network (ANN), and (iii) extreme gradient boosting (XGBoost). A total of 198 pertinent experimental datasets were compiled and included six types of FRCM composites (PBO, carbon, glass, basalt , coated carbon, and combined glass and carbon). The considered input parameters comprise the beam cross-sectional details, area of tensile and compressive steel reinforcement, mechanical properties of FRCM composite, and concrete compressive strength . To assess the reliability of ML models, four existing analytical models and one established standard guideline were used for comparison. Moreover, six statistical metrics were employed, along with an overfitting analysis, to determine the best-fitting model. Graphical fitting of the optimal model was depicted using the Taylor diagram, violin plot, as well as multi-panel histogram plot. Based on both graphical and statistical metrics, the XGBoost model attained the highest precision compared to all analytical and ML-based models. The correlation coefficient and MAPE of the XGBoost model were 0.9977% and 2.98%, respectively. To interpret the influence of individual parameters on the flexural strength of the FRCM-strengthened RC beams, a feature importance plot based on SHAP explanatory theory was deployed. Ultimately, a user-friendly graphical interface was developed and made accessible to aid practicing engineers in estimating the flexural strength of FRCM-strengthened RC beams, offering an effective alternative to complex design procedures.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".