Unveiling the combined thermal and high strain rate effects on compressive behavior of steel fiber-reinforced concrete: A novel predictive approach
Bibliographic record
Abstract
Steel Fiber Reinforced Concrete (SFRC) is widely recognized for its exceptional performance under extreme conditions, such as high temperatures and high strain rates, due to its enhanced fracture resistance and energy absorption. However, the combined effects of these extreme conditions on SFRC’s mechanical behaviour remain insufficiently explored. This study presents a novel framework for predicting the compressive strength of SFRC subjected to temperatures ranging from 200°C to 1200°C and strain rates from 10⁻⁵/s to 10²/s using advanced machine learning (ML) approaches: Gene Expression Programming (GEP), Multi-Expression Programming (MEP), and XGBoost. A dataset comprising 307 experimental results from published studies was used, with 70 % allocated for training and 30 % for testing and validation. The GEP model demonstrated superior performance with R-values of 0.964, 0.968, and 0.960 for training, validation, and testing, respectively. The MEP and XGBoost models provided reasonable accuracy but underperformed compared to GEP. Global Sensitivity Analysis (GSA) identified temperature and strain rate as the most significant parameters influencing compressive strength, while heating rate had minimal impact. Notably, the study developed a simplified empirical equation through GEP, enabling efficient and accurate strength estimation. This research addresses critical gaps by integrating advanced ML models to predict SFRC behaviour under extreme conditions, offering a reliable and cost-effective alternative to experimental testing. The findings provide valuable insights for optimizing SFRC in critical infrastructure applications, enhancing safety and resilience against fire and blast scenarios. This study’s framework sets a foundation for future work in performance-driven material design.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".