Gradient Boosting Coupled with Oversampling Model for Prediction of Concrete Pipe-Joint Infiltration Using Designwise Data Set
Bibliographic record
Abstract
Infiltration of groundwater through reinforced concrete pipe (RCP) joints under hydrostatic pressure has been a major costly challenge in municipal sewer network systems. Analysis of an exclusive designwise infiltration test data of RCP joints showed that conventional regression analysis failed to produce reliable predictions. Accordingly, tree-based machine-learning techniques including random forest, extra trees, and gradient boosting classifiers have been deployed in this study to create reliable models. A large designwise data set identifying failure of RCP joints and the effect of key design parameters was collected using a novel experimental program. Due to the resulting unbalanced experimental data set, oversampling techniques including synthetic minority over-sampling technique (SMOTE) and density based synthetic minority over-sampling technique (DBSMOTE) were employed to enhance predictive performance. Gradient boosting coupled with DBSMOTE offered a robust machine-learning model for predicting RCP joint hydrostatic infiltration. The hybrid gradient boosting classification (GBC)-DBSMOTE model achieved superior predictive accuracy in terms of several classification indicators, with promising capability to create RCP joint hydrostatic infiltration performance charts that capture the effects of key design parameters, such as pressure duration and level, pipe size, and gasket sealing. The robust predictive model could produce design charts that aid municipalities in proactively averting sewage system infiltration problems at low cost, instead of the prevailing reactive approach to this problem.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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".