A review on numerical simulation of the failure of high performance fiber-reinforced concretes
Bibliographic record
Abstract
Numerical simulations have been widely used to study the failure and mechanical behavior of high performance fiber-reinforced concretes (HPFRCs) in the past decades. According to different algorithms principles of these numerical modeling methods, this paper categorized these methods into mesh-based, particle-based, and lattice-based approaches. The main benefits and limitations of each method were introduced and analyzed with a particular focus on their application to HPFRCs. A comprehensive overview of the numerical modeling methods and their applications at macro-, meso-, and multiscale levels to study the failure of HPFRCs is presented. The paper discusses current trends and challenges in further research on numerical modeling methods for HPFRC failure, exploring novel approaches such as machine learning or AI within the context of numerical modeling. The aim of this paper is to provide scientific guidance and practical tools for studying the failure of HPFRCs through numerical simulation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".