Importance of Product-Specific Testing in Determining Durability Reduction Factor for Polyester Geogrids in High pH Conditions
Bibliographic record
Abstract
As alternative backfills become more commonplace in the construction of mechanically stabilized earth (MSE) walls, the selection of reduction factors in calculating the long-term design strength (LTDS) should be evaluated. Alternative backfill materials have the potential to approach the FHWA default reduction factors for durability. The FHWA default reduction factors for durability are based on the minimum molecular weight (Mw) and maximum carboxyl end group (CEG) concentration of the high tenacity polyester fibers used in geogrid production in conjunction with limiting the pH range of the soil backfill for polyester (PET) geogrids. However, these default reduction factors are conservative, and the default pH range is based on limited testing of a particular coated PET geogrid in environments with a pH over 9. This paper outlines the importance of product-specific testing in elevated pH backfill environments. A detailed multi-year investigation of the reduction in tensile strength of two different manufacturers’ coated PET geogrid exposed to pH values ranging between 10 and 11.4 was performed. The comparison between the two different coated PET geogrids shows the reduction in tensile strength due to submersion in elevated pH aqueous solutions can vary, even with similar Mw and CEG products. This paper illustrates the importance of product-specific testing and the conservatism built into the FHWA default durability reduction factors.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Geotechnical testing of polyester geogrid durability; the standards at issue are engineering design standards, not research standards.
It experimentally evaluates geogrid durability under alkaline conditions.
Geogrid durability product testing is civil/geotechnical engineering.
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 | 0.004 | 0.006 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".