Deterioration modeling of sewer pipes via discrete-time Markov chains: A large-scale case study in the Netherlands
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.... Mehr ...
Verfasser: | |
---|---|
Dokumenttyp: | conferencePaper |
Erscheinungsdatum: | 2022 |
Schlagwörter: | degradation modeling / discrete-time Markov chain / sewer pipe network / large-scale case study / reliability engineering |
Sprache: | unknown |
Permalink: | https://search.fid-benelux.de/Record/base-26847792 |
Datenquelle: | BASE; Originalkatalog |
Powered By: | BASE |
Link(s) : | https://zenodo.org/record/6535854 |
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 8 cohorts of interest. Two types of discrete-time Markov chains (DTMC), where one contains additional transitions compared to the other, are commonly used to model sewer pipe degradation at the pipeline level, and we want to evaluate which suits better our case study. Here we provide supplementary figures associated with the results obtained in this publication. ; This research has been partially funded by NWO under the grant PrimaVera (https://primavera-project.com) number NWA.1160.18.238, and has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 101008233.