Explainable AI determines maintenance priority for airport stormwater pipe networks

Summary

The study presents an explainable machine learning framework that prioritizes maintenance of airport rainwater pipe networks using Random Forest and gradient-boosting models.

Why it matters

Effective prioritization can reduce costly interventions and extend the lifespan of critical drainage infrastructure at airports, where failures have high operational impacts. By integrating explainable AI, the approach provides transparency for engineering decisions and aligns with sustainable asset management goals.

Critical take

The study lacks a publicly available dataset and real-world validation, making it difficult to assess the practical effectiveness of the framework beyond its conceptual stage.