Novel soft computing hybrid model for predicting shear strength and failure mode of SFRC beams with superior accuracy
Bibliographic record
Abstract
The ability of steel fibers to enhance the shear strength and post-cracking behavior of plain concrete stimulated remarkable increase in using steel fiber-reinforced concrete (SFRC) in construction. However, steel fibers increase the complexity of assessing the shear behavior. Developing accurate models to estimate the shear capacity is crucial to satisfying requirements of design codes. While various empirical models have been developed for this purpose, they suffer from multiple shortcomings. Machine learning techniques have recently emerged as a strong contender for mitigating such drawbacks and providing better accuracy. In this study, a novel metaheuristic atom search optimization (ASO) algorithm based on molecular dynamics was coupled with artificial neural networks (ANN) to forecast the shear capacity of SFRC beams and overcome drawbacks of standalone models. Moreover, four classification models (naïve Bayes, support vector machine (SVM), decision tree, and k-nearest neighbor) were used to forecast the failure mode of SFRC beams. Performance assessment of the models revealed that the ASO-ANN model achieved most reliable predictive accuracy for shear strength, while the k-nearest neighbors model was the most accurate for failure mode classification. The ability to predict simultaneously the shear strength and failure mode with superior accuracy opens immense opportunities for the shear design of new SFRC beams and for selecting innovative retrofitting strategies for existing shear deficient 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".