Time domain reflectometry (TDR) potential for soil condition monitoring of geotechnical assets
Bibliographic record
Abstract
The performance of geotechnical assets is influenced by various external factors including time and changing loading and environmental conditions. These changes could reduce the asset’s ability to maintain its function, potentially resulting in failure, which could be extremely disruptive and expensive to remediate; thus, the ability to monitor the long-term condition of the ground is clearly desirable as this could function as an early-warning system, permitting intervention prior to failure. This study demonstrates, for the first time, the potential of using time domain reflectometry (TDR) for long-term monitoring of the relative health of an asset (via water content and dry density) in a field trial where a clayey sandy silt was exposed to leaking water from a pipe. TDR sensors were able to provide detailed information on the variation in the soil conditions and detect abrupt changes that would relay a prompt for asset inspections or interventions. It is proposed that TDR could be used alone or together with other shallow geophysical techniques for long-term condition monitoring of critical geotechnical assets. Early-warning systems could be based on thresholds defined from the values or the relative change of the measured parameters.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".