Diagnostic Assessment Considering Complex Damage to FireDamaged Structures
Bibliographic record
Abstract
Generally, building fires cause direct damage to human lives and property, and can also result in indirect damage due to the absence of residential and work spaces during the recovery process.Various preliminary studies have been conducted to minimize the direct damage caused by building fires, resulting in the improvement of design methods, enhancement of material performance, and legal supplements that are being applied in construction sites.Furthermore, minimizing indirect damage can be achieved by applying appropriate recovery measures through rational and prompt diagnostic procedures.However, research on rapid and rational diagnostic procedures for damage recovery has been relatively insufficient.Building fires cause phenomena such as deformation, cracking, spalling, accelerated carbonation, deterioration of material performance, and reduction in residual strength of structural members, leading to complex damage rather than damage by a single factor.Therefore, the diagnostic process of fire-damaged buildings requires a thorough investigation of the damage scale by setting all chemical and physical phenomena occurring in fire-exposed structures as evaluation items, and deriving diagnostic results based on the investigation content.However, existing diagnostic methods for fire-damaged buildings cannot combine the damage of all evaluation items such as deformation, spalling, cracking, carbonation, and heating temperature.It can only present damage grades for each item, making it impossible to derive a rational diagnostic result for fire-damaged buildings.Additionally, the method of estimating heating temperature, which is the most crucial factor in diagnosing fire-damaged buildings, has limitations.It involves visual assessment of concrete discoloration, deformation or melting states of finishing materials, along with various non-destructive testing methods that can yield results significantly different from the actual heating temperature.Estimation results of heating temperatures with large deviations from the actual fire temperature can lead to entirely different evaluations of the fire damage scale.Different results depending on the diagnostician can significantly expand the damage scale.Therefore, this study aims to propose a rational diagnostic method that overcomes the problems of existing diagnostic methods by minimizing the empirical judgment process of evaluators and performing diagnostics by comprehensively assessing the complex damage items caused by fire.Additionally, to ensure consistent diagnostic results, even when different evaluators perform diagnostics on fire-damaged buildings using the proposed method, the study aims to secure reliability through rational case studies and verification processes for each evaluation item.
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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".