Investigating Sewer Corrosion Drivers Using Quantitative Modeling
Bibliographic record
Abstract
The corrosion of concrete sewers presents a serious challenge to urban infrastructure, threatening the structural integrity and operational efficiency of wastewater systems worldwide.Corrosion occurs through chemical and biological reactions, where the formation of sulfuric acid due to microbial activity leads to progressive deterioration of concrete surfaces, ultimately weakening the material and accelerating failure.This degradation increases maintenance costs, shortens infrastructure lifespan, and poses risks to public health and environmental safety.Despite extensive research on sewer corrosion, most studies focus on individual factors rather than conducting a comprehensive assessment of all contributing factors.This study addresses this gap by systematically identifying and evaluating the most influential factors driving sewer corrosion.A multi-step methodology was employed, including a literature review to identify key sewer corrosion drivers (SCDs), an expert survey targeting professionals in infrastructure and environmental engineering, and a statistical analysis of the collected data.The findings were further validated using Partial Least Squares Structural Equation Modeling (PLS-SEM) to establish relationships between these factors.The results indicate that environmental drivers (ED) exert the strongest influence on sewer corrosion, followed by operational drivers (OD) and, finally, pipe-related drivers (PRD).These findings offer practical implications for infrastructure engineers, policymakers, and wastewater management agencies by guiding preventive maintenance strategies, optimizing material selection, and improving sewer system design.By implementing these insights, municipalities can enhance the resilience of wastewater infrastructure, minimize unexpected failures, and promote sustainable urban development.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".