Footprint of construction errors on the structural damages
Bibliographic record
Abstract
The majority of structural failures are attributable to errors in construction. This problem exists in all countries, but it is more frequent in developing communities. This study focuses on construction errors of structures in Tehran, the capital city of Iran. In this study, eighty-eight buildings have been investigated during the construction phase. These buildings have been categorized into ten types and have been distributed in twenty-two suburbs. Results showed that the buildings of Tehran can suffer from at least forty-nine major construction problems. In addition, for the first time, this research has introduced the following three terms in relation to prioritizing of construction errors: Relative Importance Factor (RIF), Priority Index (PI) and Structural Importance Index (SII). As a part of the conclusions, the results showed one hundred percent of investigated buildings are affected dramatically by the “use of untrained workers” and “lack of sampling or wrong sampling” too. In this regard, the RIF and PI of each “Lack of sampling or wrong sampling” and “use of untrained workers” are 100 and 1, respectively. Also, suburb 3 has the best construction conditions while suburb 10 has the worst.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".