Operational based stochastic cluster regression-based modeling for predicting condition rating of highway tunnels
Bibliographic record
Abstract
Despite the higher capital costs of tunnels and supplementary cost of maintenance, fewer deterioration models have been built compared with other highway components. Most of these models were limited to structural defects, not operational conditions. Moreover, there are inherent subjectivity and inaccuracy of the developed models, which may affect the maintenance process. The aim of this research and proposed contribution are investigating and modeling the impact of explanatory variables and non-periodic maintenance effect on highway tunnel condition. The stochastic regression analysis has been conducted to come out with a realistic tunnel condition through the Monte Carlo simulation methodology. The research methodology consists of three phases: cluster analysis, regression modeling, and stochastic analysis. A dataset of 473 highway tunnels along 41 American states from the National Tunnels Inventory (NTI) has been used. Nine models have been developed with a high coefficient of determination (R2 = 90.8%). The obtained results and the models could help advance the development of tunnel deterioration models from a management perspective, not only from the structural view. The developed models help highway authorities to prioritize the maintenance and objectively make informed investment decisions based on the historical data. This research has come as a response to the significant problems facing the highway authorities regarding tunnels and asset management.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".