Assessment of Infrastructure Reliability in Expansive Clays Using Bayesian Belief Network
Bibliographic record
Abstract
Civil infrastructure supported by expansive clays is severely affected by extensive volumetric deformations. The reliability prediction of such facilities is quite challenging because of the complex interactions between several contributing factors, such as a scarcity of data, a lack of analytical equations, correlations between quantitative and qualitative information, and data integration. The main contribution of this research is the development of a modeling approach based on the Bayesian belief network. The modeling results highlight that facility age is the most critical parameter (23% variance), followed by facility type (1.37% variance), for all the investigated types of infrastructure, namely road embankments, buried pipelines, and residential housing. Likewise, the results of sensitivity analysis and extreme scenario analysis indicate that the new method is capable of predicting infrastructure reliability and the assessments were found to be in agreement with expected field behavior. The proposed model is useful in decision making related to civil infrastructure management in expansive clays.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".