Performance of two geosynthetic reinforced walls with recycled construction waste backfill and constructed on collapsible ground
Bibliographic record
Abstract
ABSTRACT: Geosynthetic reinforced soil walls are now an accepted technology for the solution of earth-retaining problems due to cost savings, easy and quick construction, and associated environmental benefits. Additional savings and reduction in environmental impact can be realised by using recycled construction and demolition waste (RCDW) as the backfill material. This paper describes two such structures that were built to full scale and instrumented. One of the walls was reinforced with a woven polyester geogrid (wall 1) and the other (wall 2) with a relatively more extensible nonwoven polypropylene geotextile. Both walls were constructed using RCDW as backfill material and were built on a foundation soil prone to fabric collapse due to increased stress and/or increase in moisture content. During the monitoring period the walls were subjected to a rainy season followed by induced inundation of the foundation to trigger soil fabric collapse. The results showed that foundation soil collapse influenced wall behaviour more than geosynthetic type. The exception to similar performance was local face bulging which was greater for wall 2 (geotextile) with the more extensible reinforcement under unconfined conditions than for wall 1 (geogrid) which was expected in the moving formwork construction method. However, directly behind the wall face where both reinforcement material types were confined the horizontal displacements were similar. In addition, at locations beyond half of the wall base length the strain distributions were low (1% or less) for both walls. A practical conclusion from this study is that if the wrap-face appearance at end of construction is not a concern (i.e. large bulging) then wall performance is unaffected by the choice of reinforcement types used in this investigation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".