Novel Reliability Evaluation of Existing Structures Using Digital Image Processing and Random Finite Element Simulation
Why this work is in the frame
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Bibliographic record
Abstract
Reinforced concrete (RC) structures are susceptible to deterioration as a result of concrete cracking and steel corrosion. Evaluations of current RC structures are undertaken to ascertain adherence to building codes, the necessity for enhancements, or to rectify structural inadequacies. Reliability techniques are utilized to quantitatively assess the structural integrity of existing RC structures, taking into account the inherent uncertainties related to the applied loads and the resistance of the structure. The main difficulty in evaluating the safety of current RC structures, namely in determining the reliability index, is to formulate accurate resistance models. This problem is particularly pronounced when observable indications of structural deterioration, such as cracking and steel corrosion, are evident. The primary aim of this research is to implement a new computational framework for evaluating the structural integrity of RC elements. This framework utilizes digital image processing (DIP) techniques in conjunction with random finite element (RFE) simulation. In this approach, actual images of the structure under investigation are employed to construct finite element (FE) models, while random fields are utilized to represent the spatial variability in material properties. The recommended framework is proven to enhance the accuracy of the reliability estimate by mitigating the uncertainty associated with the concrete crack pattern and minimizing the uncertainty related to the corrosion process in steel.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it