Composition of demolition wastes from Chi-Chi earthquake-damaged structures and the properties of their inert materials
Bibliographic record
Abstract
The disastrous Chi-Chi earthquake struck Taiwan on 21 September 1999 and resulted in the creation of more than 20 million cubic metres of demolition waste, which was stored temporarily at several sites. To reduce the site clearing costs, the inert materials (concrete, brick, pottery, and fines) in the wastes should be reclaimed and recycled immediately; but most of their properties were not known. Therefore, this study investigates the properties of demolition wastes, including inert materials, coarse portions, and fines obtained from two earthquake demolition waste storage sites (Puli and Tali) and one normal urban construction waste storage site (Taichung). It is found that the volumetric content of inert materials from earthquake demolition waste is about 96%. Such inert material is classified as having an excellent subgrade rating and can be reused as subbase and base soils. The abrasion level of the coarse portions of inert materials from the three sites meets the required level of coarse aggregate according to Standard Taiwan Code CNS-490. On the other hand, the maximum dry unit weights of fines of inert materials from the three sites are greater than 14.7 kN/m 3 , and their shear strengths are higher than that of the same classification group (SW–SM) of natural soils, so they can be reused as construction soils.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".