The degradation of sewer pipes poses significant economical, environmental
and health concerns. The maintenance of such assets requires structured plans
to perform inspections, which are more efficient when structural and
environmental features are considered along with the results of previous
inspection reports. The development of such plans requires degradation models
that can be based on statistical and machine learning methods. This work
proposes a methodology to assess their suitability to plan inspections
considering three dimensions: accuracy metrics, ability to produce long-term
degradation curves and explainability. Results suggest that although ensemble
models yield the highest accuracy, they are unable to infer the long-term
degradation of the pipes, whereas the Logistic Regression offers a slightly
less accurate model that is able to produce consistent degradation curves with
a high explainability. A use case is presented to demonstrate this methodology
and the efficiency of model-based planning compared to the current inspection
plan.

该研究提出了一种评估污水管道降解模型适用性的方法，并表明 Logistic Regression 作为一种较少准确但能够产生一致降解曲线和具有高可解释性的模型，在制定检查计划方面比集成模型更加高效。