Damages Identification Methodology of Unseen Reinforced Concrete Foundations Using Error Analysis of Transfer Resistance
Bibliographic record
Abstract
Natural disasters such as earthquakes and tsunamis have constantly proved the fragility of infrastructures and shown the complexity of strong ground motion generation.To reduce the vulnerability of infrastructures, numerous methodologies have been developed to extract detailed information from the earthquake source mechanism to provide rational earthquake hazard information and use it as input for structural vulnerability assessment.A common practice after a destructive earthquake occurs is to observe and analyse the damage mechanisms on the structures distributed in the damage zones, aiming to comprehend and infer the process and reasons for such destructive damages.To later upgrade the seismic standards for structural analysis and engineering design practices from the outcomes of the lesson learned from this devastating experience, to level up decision-making associated with diagnosis and strengthening of vulnerable infrastructures, and to contribute to decision-making related to strengthening of infrastructures, susceptible to earthquake damages, we proposed an earthquake damages detection methodology for unseen underground bridge infrastructures based on transfer resistance as essential measurement, and combined along with seasonal external effects, and error analysis; to consider the dispersive and propagation nature of uncertainty, and account for correction factors, respectively.To unravel the dimensional and intrinsic error associated with the fracturing area within the foundation and observed over the residual power spectrum.In general, we detected the earthquake damage zone when the reinforcement mesh was exposed and surpassed by the rupture zone.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".