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

Deterioration modeling of sewer pipes via discrete-time Markov chains: A large-scale case study in the Netherlands