Experimental Evaluation of Deteriorated CMPs Retrofitted by Different Non-invasive Approaches
Bibliographic record
Abstract
Corrugated metal pipes (CMPs) deployed across North America are in various states of deteriorations with diminishing structural health that can cause road failure and pose serious threat to public safety. This paper presents an extensive experimental study conducted on deteriorated and retrofitted CMPs, and compares the results with available analytic approach — Modified Iowa equation. Simulated deterioration was performed on the new CMPs using mechanical approach and later, those CMP specimens were retrofitted using four different non-invasive methods. The specimens were tested under five different overburden pressures. Responses of the soil-pipe systems for deteriorated and rehabilitated specimens in terms of surrounding soil pressure and deformations at crown, spring-line, and invert were recorded and compared. It was found that the soil envelops and the CMPs experienced considerable change in pressure and deflections, respectively due to deterioration. However, rehabilitation using all the invasive approaches helped to regain soil pressures and deflections close to the original state, indicating their viability. The measured deflections from experimental studies were also compared with the predicted values obtained from the Modified Iowa equation. Such comparison is of immense importance to establish design guidelines for rehabilitated liner-CMP culvert systems.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".