Effects of Geogrid Reinforcement on the Backfill of Integral Bridge Abutments
Bibliographic record
Abstract
The construction of integral bridges is one of the most effective methods to reduce bridges’ construction and in-service costs. However, there are associated geotechnical problems with their abutments backfill due to the integrated abutments. The main goal of this study is to evaluate and quantify the benefits of geogrid reinforcement for reducing the backfill’s geotechnical problems. For this purpose, using small-scale physical modeling, the benefits of geogrid reinforcing of the backfill of an integral abutment bridge subjected to cyclic movements are evaluated. The results are then compared with a previous study performed on unreinforced backfill and two types of geocells. In this study, 120 loading cycles are applied to geogrid-reinforced soil to simulate the cyclic loadings on integral abutment backfill due to seasonal abutment displacement. The horizontal reaction load at the top of the wall, changes in pressure behind the wall, and deformation in backfill soil are measured during the test. Then the results are discussed in terms of equivalent peak lateral soil coefficient (Kpeak), lateral earth pressure coefficient (K*), and normalized settlement behind the wall (Sg/H). The derived lateral soil coefficients and settlement behind the abutment show that geogrid substantially reduces pressure and settlements after 120 cyclic loads. Based on the results, Kpeak and K* of the geogrid-reinforced backfill decrease by up to 36%, and Sg/H behind the wall decreases by 62%. In addition, the comparison of the results for geogrid with two geocell types shows that geogrid is more efficient in terms of lateral soil coefficients.
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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 | 0.000 | 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) | 0.001 | 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".