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Synapse
March 29, 20260 citationsOpen Access

Defect Evolution in Sewer Pipes: Enhancing Deterioration Models

LGLukas GuerickeADAntoine DauratHSHauke Sonnenberg

Key Points

  • This research aims to determine if modeling individual defect evolution can enhance sewer pipe deterioration models.
  • Analyzed defect transitions in multi-inspected sewer pipes using extensive inspection data from Berliner Wasserbetriebe.
  • Created a transition matrix and knowledge graph to map defects and their inter-dependencies.
  • Evaluated 24,734 inspection pairs for deterioration patterns.
  • Identified plausible defect transitions, including gradual degradation from roughness to missing pipe wall parts.
  • Found varying durations in defect transitions, alongside some transitions indicating possible inspection uncertainties.

Abstract

Deterioration models for sewer pipes often rely only on aggregated pipe-level data (pipe condition), without considering individual defects and their evolution. Is-it worth considering individual defects to improve deterioration models? A preliminary answer is to know if it is possible to model the evolution of individual defects. This study presents a methodology for analysing defect transitions in multi- inspected sewer pipes to gain insights into the aging and deterioration processes at the defect level. Using inspection data provided by Berliner Wasserbetriebe, covering 242,920 pipes and nearly 1.9 million observations, incl. defects encoded according to EN 13508-2, defect transitions were analysed across 24,734 inspection pairs. Defects between inspection pairs for each pipe and position are mapped, creating a transition matrix and knowledge graph to highlight defect inter-dependencies. The results reveal plausible transitions, such as gradual surface degradation from increased roughness to missing pipe wall parts, with varying durations, but also transitions that may reflect inspection uncertainties. Future work will incorporate defect severity classes and explore how these insights can enhance machine learning models through feature engineering or domain-informed approaches.

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Cite This Study

Guericke et al. (2026) studied this question.

synapsesocial.com/papers/69c8c2a4de0f0f753b39d109https://doi.org/10.71573/rv969a25
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Predicting Degradation Patterns in Water Distribution Networks and Their Influence on Pipe Failure2025
  2. 2A mechanistic deterioration point assignment model for water pipe condition assessment2024
  3. 3Novel Sewer Defect Prediction Leveraging Advanced Machine Learning (ML) Models2026
  4. 4Sewer pipes’ lifespan prediction: can we modify the data to make the machine learning algorithms fit the purpose?2026
  5. 5Maintenance Strategies for Sewer Pipes with Multi-State Degradation and Deep Reinforcement Learning2024 · 6 citations