Residual Capacity Of Fatigue-Prone Reinforced Concrete Structures
Bibliographic record
Abstract
Designing reinforced concrete structures susceptible to fatigue damage requires satisfying the fatigue limit state.Current codes' stress-life curve considerations are confined to the design stages and cannot be used to monitor evolving fatigue damage if the structure in service is subjected to severe environmental conditions or loads that are higher than the design values.One significant observation in the area of fatigue damage is the scatter in results from various stresslife models.In addition, the use of S-N curves in areas of high local stresses exceeding stress-life curve limits does not necessarily mean global failure, especially if the structural stability is not affected.Reports on collapsed wind turbines due to foundation failure often indicate wind farm shutdown periods to enable investigations.The lack of means to study damage evolution or observe residual capacity of such reinforced concrete structures often results in the recommendation to rehabilitate all the foundations in most cases; hence, incurring significant losses.This report proposes an innovative approach that utilizes finite element analysis framework to predict the damage evolution and the residual capacity of reinforced concrete structures after a given number of fatigue loading cycles.The advantage of this approach stems from the fact that recorded wind loads and cycles from wind turbines can be read into the algorithm and the variations in loading amplitudes are considered in the analyses.To verify the model's reliability, fatigue life and residual capacity estimations are conducted on reinforced concrete specimens that were tested under fatigue loading.The results obtained portrayed substantial correlation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".