Numerical assessment and prediction method for the chemico-mechanical deterioration of ASR-affected concrete structures
Bibliographic record
Abstract
The degradation of concrete structures by the alkali–silica reaction (ASR) is highlighted in recent years in civil engineering. To quantify the deterioration of concrete structures, one has the behavior modeling of affected concrete proposed by multiple approaches, on one side, and the expansion of the structure measured by the in situ engineers, on the other side. The task is to determine the progressive structure degradation from these two supports by a systematical method. Nevertheless, this method should not depend on the employed model so that a better one can always be added to the assessment. Moreover, one should also be able to analyze the credibility of the assessment result. All these points differentiate between a laboratory expertise and a structural assessment. This article proposes a time-scaled evaluation method and formulates it into an inverse problem. With an adopted chemico-mechanical model for ASR expansion and the analyzed structure observation data, the authors give the numerical solution for the established inverse problem. An ASR-affected suspension bridge is then evaluated using a finite element code for its durability by the achieved method. Some further discussions are given in the last section of this article. Key words: alkali–silica reaction, modeling, inverse problem, assessment, suspension bridge, durability.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".