A machine learning approach for predicting a full load-deflection behaviour of strengthened beams using fabric-reinforced cementitious matrix (FRCM)
Bibliographic record
Abstract
Machine learning (ML) methods in structural engineering are typically applied to predict single values, such as failure load. This study presents an advanced application of ML by predicting five critical points on the load-deflection curve of fabric-reinforced cementitious matrix (FRCM)-strengthened rectangular beams. Three separate ML models were developed, each tailored to predict specific key points on the curve. The ML models consider variables such as mechanical and geometric properties of a beam, steel reinforcement, and the FRCM properties such as nominal thickness, number of layers, type of fabric and presence of anchorage. Based on these models, a user-friendly application was developed. In the second part of this study, validated Finite Element (FE) models examine the robustness of ML models on unseen data. The accuracy of the load-deflection curves is evaluated using three parameters: load capacity, stiffness, and absorbed energy. Results indicate that the proposed ML-based models effectively capture the entire response of FRCM-strengthened beams, achieving RMSE of 15 kN, 6 kN/mm, and 2kN-m for load capacity, stiffness, and absorbed energy, respectively. • Three ML models accurately predict the load-deflection behavior of FRCM-strengthened beams. • ML-based predictions are faster than FE methods with comparable accuracy. • Increasing FRCM layers improves strength until debonding dominates at higher layers. • Engineers can quickly assess strengthening designs without extensive testing or FEM. • Load capacity, stiffness, and absorbed energy predictions align well with experiments.
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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) | 0.000 | 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".