A database and model to support proactive management of sediment-related sewer blockages

Due to increasing customer and political pressures, and more stringent environmental regulations, sediment and other blockage issues are now a high priority when assessing sewer system operational performance. Blockages caused by sediment deposits reduce sewer system reliability and demand remedial action at considerable operational cost. Consequently, procedures are required for identifying which parts of the sewer system are in most need of proactive removal of sediments. This paper presents an exceptionally long (7.5 years) and spatially detailed (9658 grid squares – 0.03 km2 each – covering a population of nearly 7.5 million) data set obtained from a customer complaints database in Bogotá (Colombia). The sediment-related blockage data are modelled using homogeneous and non-homogeneous Poisson process models. In most of the analysed areas the inter-arrival time between blockages can be represented by the homogeneous process, but there are a considerable number of areas (up to 34%) for which there is strong evidence of non-stationarity. In most of these cases, the mean blockage rate increases over time, signifying a continual deterioration of the system despite repairs, this being particularly marked for pipe and gully pot related blockages. The physical properties of the system (mean pipe slope, diameter and pipe length) have a clear but weak influence on observed blockage rates. The Bogotá case study illustrates the potential value of customer complaints databases and formal analysis frameworks for proactive sewerage maintenance scheduling in large cities.

► An extensive and detailed sediment-related blockage data set is presented.
► Insight into why the commonly applied HPP model may not be generally suitable.
► New applications of methods for identifying clustering and non-stationarity of failures.
► Identification of sewer properties affecting average blockage rates.
► An analysis framework easily transferable to yet under-exploited data bases.