Modeling the shear capacity of externally bonded fiber reinforced polymer strengthened beams by artificial neural network
Bibliographic record
Abstract
The current guidelines, namely ACI 440.2R and fib TG-9.3, quantify the nominal shear strength of strengthened reinforced concrete (RC) beams by simply summing up the shear strength contribution of the three components: concrete (Vc), steel stirrups (Vs), and externally bonded fiber reinforced polymer (EB-FRP) (Vf). However, as reported in the literature, this assumption is inaccurate due to an adverse interaction between EB-FRP and steel stirrups. This research study adopts a machine learning method called Artificial Neural Network (ANN) to model EB-FRP shear strength contribution. The model is built by considering the possible interaction mentioned above. Considering the collective response of these three components is crucial in accurately predicting the shear behavior because the performance potential of EB-FRP strengthening depends on the material properties of existing members. To implement the ANN modeling method, a database of 191 test specimens reported by a total of 40 individual studies is gathered. For the assessment of the proposed ANN model, a sensitivity analysis is conducted. The proposed model prediction is then compared statistically with experimental results and with the predictions of the existing guidelines such as ACI 440.2R and fib TG-9.3. The results showed that the developed ANN model could predict the EB-FRP shear strength contribution with higher accuracy than the current guidelines. Moreover, a user-modified executable program is also developed in this study to readily execute the proposed ANN model by inputting the properties of the existing beam and EB-FRP.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".