Data augmentation using conditional generative adversarial network (cGAN): applications for sewer condition classification and testing using different machine learning techniques
Bibliographic record
Abstract
ABSTRACT The increasing availability of condition assessment data highlights the challenge of managing data imbalance in the asset management of aging infrastructure. Aging sewer pipes pose significant threats to health and the environment, underscoring the importance of proactive management practices to enhance asset maintenance and mitigate associated risks. While machine learning (ML) models are widely employed to model the complex deterioration process of sewer pipes, they face performance limitations when trained on imbalanced condition grade data. This paper addresses this issue by proposing a novel approach using conditional generative adversarial network (cGAN) for data augmentation. By generating synthetic data for minority classes, the skewed distribution of the sewer dataset is balanced, facilitating more robust and accurate predictive models. The utility of the proposed method is evaluated by training different ML classifiers, including neural network (NN), decision tree, quadratic discriminant analysis, Naïve Bayes, support vector machine (SVM), and K-nearest neighbor. Quadratic discriminant, Naïve Bayes, NN, and SVM classifiers demonstrated improvement. The cGAN-based data augmentation method also outperformed two other data imbalance handling techniques, random under-sampling, and cost-sensitive NN. Consequently, data generated by cGAN can effectively aid asset management by developing proactive classifiers that accurately predict pipes at a high risk of failure.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".