Integrated high-resolution electrical tomography and drilling cascading investigation of earth-rock dam leakage in a siltstone region: A case study of the Maoshan Reservoir, China
Bibliographic record
Abstract
Abstract Piping, erosion at the contact of flowing soil, and leaching damage pose a serious leakage risk to earth-rock dams in the siltstone regions. With increasing reservoir operation time, seepage phenomena commonly occur in earth-rock dams, leading to progressively severe damage to the dam body. To understand the distribution of seepage in an earth-rock dam, including its location and magnitude, an imaging analysis of the dam structure is necessary. This involves determining the leakage positions and the underlying reasons. In this study, we conducted a high-resolution electrical resistivity tomography (HERT) investigation to characterize the leakage conditions of the Maoshan Reservoir earth-rock dam in the siltstone area of Jiangxi Province. To enhance the reliability of the HERT data interpretation, drilling, and water pressure tests were employed to validate the interpretations of low-resistivity zones. By applying this cascading survey and analysis method, we obtained spatial variability information on the apparent resistivity of the dam. The resistivity imaging of the entire dam revealed significant variations in apparent resistivity in the study area. Low-resistivity anomalies and high-resistivity anomalies were observed on both the upstream and downstream slopes of the dam. These anomalies represent saturated water channels and impervious bodies, with resistivities less than 200 Ωm and greater than 700 Ωm, respectively. The results of this study indicate that a cascading survey combining HERT and drilling is effective for analyzing the leakage positions of earth-rock dams, providing valuable insights for engineers in implementing effective anti-leakage measures in siltstone area reservoirs.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".