Defect Detection and Characterization in Soil Bentonite Cutoff Wall Using Electrical Resistivity
Bibliographic record
Abstract
Defects in soil-bentonite (SB) slurry trench cutoff walls may reduce the effectiveness of the engineered structure to minimize groundwater flow and contain pollutants. One potential construction defect identified for SB cutoff walls is the presence of granular material in the wall due to sidewall collapse or sedimentation on the backfill slope and/or in the trench key. However, post-construction methods that could be used to detect these defects are relatively untested or the analysis of the results is very complicated. In this study, electrical resistivity (ER) was used in unique configurations in an experimental SB cutoff wall installed in a well characterized alluvial formation to detect and characterize designed defects placed during construction. Defects were placed in the middle of wall at depths of 1 or 4 m below the top of the wall, well above the trench key at ~7 m, and were also placed on the trench key. The granular defects ranged in size from 0.02 to 0.3 m3 and were predominantly permeable sandbags with a clean, well sorted (poorly graded) medium sand. The largest defect installed in the wall was an impermeable limestone boulder. Custom electrodes were developed that could be pushed into the SB cutoff wall and thus collect ER data both at the ground surface and at depth within the wall. The ER data indicate that while it is possible to detect the defects, the sidewalls of the trench impart a significant effect on the ER data and thus affect the maximum distance between the observation electrodes and the potential defect.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".