  Sewer pipe network systems are an important part of civil infrastructure, and
in order to find a good trade-off between maintenance costs and system
performance, reliable sewer pipe degradation models are essential. In this
paper, we present a large-scale case study in the city of Breda in the
Netherlands. Our dataset has information on sewer pipes built since the 1920s
and contains information on different covariates. We also have several types of
damage, but we focus our attention on infiltrations, surface damage, and
cracks. Each damage has an associated severity index ranging from 1 to 5. To
account for the characteristics of sewer pipes, we defined 6 cohorts of
interest. Two types of discrete-time Markov chains (DTMC), which we called
Chain `Multi' and `Single' (where Chain `Multi' contains additional transitions
compared to Chain `Single'), are commonly used to model sewer pipe degradation
at the pipeline level, and we want to evaluate which suits better our case
study. To calibrate the DTMCs, we define an optimization process using
Sequential Least-Squares Programming to find the DTMC parameter that best
minimizes the root mean weighted square error. Our results show that for our
case study, there is no substantial difference between Chain `Multi' and
`Single', but the latter has fewer parameters and can be easily trained. Our
DTMCs are useful to compare the cohorts via the expected values, e.g., concrete
pipes carrying mixed and waste content reach severe levels of surface damage
more quickly compared to concrete pipes carrying rainwater, which is a
phenomenon typically identified in practice.
