  Sewer pipe systems are essential for social and economic welfare. Managing
these systems requires robust predictive models for degradation behaviour. This
study focuses on probability-based approaches, particularly Markov chains, for
their ability to associate random variables with degradation. Literature
predominantly uses homogeneous and inhomogeneous Markov chains for this
purpose. However, their effectiveness in sewer pipe degradation modelling is
still debatable. Some studies support homogeneous Markov chains, while others
challenge their utility. We examine this issue using a large-scale sewer
network in the Netherlands, incorporating historical inspection data. We model
degradation with homogeneous discrete and continuous time Markov chains, and
inhomogeneous-time Markov chains using Gompertz, Weibull, Log-Logistic and
Log-Normal density functions. Our analysis suggests that, despite their higher
computational requirements, inhomogeneous-time Markov chains are more
appropriate for modelling the nonlinear stochastic characteristics related to
sewer pipe degradation, particularly the Gompertz distribution. However, they
pose a risk of over-fitting, necessitating significant improvements in
parameter inference processes to effectively address this issue.
