Utilizing Data-Driven Methods to Predict the Fatigue Life of Cement Concrete Considering Corrosive Environmental Factors
Bibliographic record
Abstract
The primary objective of this study is to assess the fatigue resistance of cement concrete when exposed to corrosive environments. To achieve this, experimental results from high-cycle fatigue (HCF) and low-cycle fatigue (LCF) tests conducted on cement concrete samples subjected to various corrosive conditions were used. Various data-driven techniques, including multiple linear regression (MLR), Taguchi sensitivity analysis (TSA), and response surface method (RSM) were utilized. The aim was not only to identify the most influential parameter affecting fatigue life but also to offer a simpler and cost-effective alternative to experimental approaches. Consequently, two key parameters related to the corrosive environment: pH value and immersion time, along with the cyclic force applied to the concrete samples as input variables across different approaches were considered. The number of cycles until sample failure regarded as the output variable in all analyses. Furthermore, the analyses were conducted with the assumption that longer fatigue life is preferable. The findings revealed that the fatigue life of Portland cement concrete consistently decreased with increasing immersion time. Notably, the pH value emerged as the most significant parameter, while the other two factors exhibited equivalent impacts.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".