Pipe failure predictions in drinking water systems using satellite observations

Abstract

Soil deformation is believed to play a crucial role in the onset of failures in the underground infrastructure. This article describes a method to generate a replacement-prioritisation map for underground drinking water pipe networks using ground movement data. A segment of the distribution network of a Dutch drinking water company was selected as the study area. Failure registration data comprising 868 failures registered over 40 months and geographical network data were obtained from the water utility. Ground movement was estimated using radar satellite data. Two types of analyses were performed: cell and pixel based. For the cell-based analysis, asbestos cement (AC) pipes exhibited the highest failure rates. Older AC pipes were also shown to fail more often, whereas failure rates for PVC were the lowest. For the pixel-based analysis, ground movement was demonstrated to play a role in the failure of all materials combined. Therefore, a replacement-prioritisation map for AC was generated which combined ground movement data and pipe-age data. This method can be a beneficial resource for network managers for maintenance and continuous monitoring.

Knowledge Management and Operational Capacity in Water Utilities, a Balance between Human Resources and Digital Maturity—The Case of AGS

“…Non-revenue water was selected to characterize the current condition of a distribution network, a common performance indicator mentioned in the literature [47][48][49][50]. Network rehabilitation is a common research subject within the context of strategic asset management [51,52] that assumes that limited resources have to be invested [53] in order to guarantee the maintenance of the level of service. Therefore, network rehabilitation was selected, as it allows for characterizing the long-term infrastructural sustainability of a utility.…”

Asset management analytics for urban water mains: a literature review

“…To what extent have the models met the expectation to predict the probability of future failures, time to next failure, and failure rate of pipe, or to predict whether or not a break will happen? • Failures reportedly could result from a large variety of factors: physical factors (e.g., pipe age, diameter, material, length, and wall thickness), environmental factors (e.g., soil type, climate, freeze/thaw properties, pipe bedding, trench backfill, traffic, and groundwater), and operational factors (e.g., number of pervious failures, water quality, internal water pressure, transient pressure, and leakage) (Stamou et al 2000;Wang et al 2009;Arsénio et al 2015;Lin and Yuan 2019;Karimian et al 2021). What are the main issues in data acquisition and quality?…”

A risk component-based model to determine pipes renewal strategies in water distribution networks

“…From 2009 until 2020, some of the studies carried out in the WDN renewal field focused on ranking rehabilitation activities by using risk-based methods (Christodoulou et al 2009, Bicik 2010, Devera 2013, Sargaonkar et al 2013, Choi and Koo 2015, D'Ercole et al 2018, Salehi et al 2018b, Salehi et al 2019. In particular, Pipe Failure Risk (PFR) was considered in some of these studies (Rogers and Grigg 2009, Bicik 2010, Harvey et al 2013, Arsénio et al 2015, Kakoudakis et al 2017, Wilson et al 2017, Mazumder et al 2018, Winkler et al 2018, Tang et al 2019. The concept of pipe risk in WDNs refers to the multiplication of Pipe Failure Probability (PFP) and Pipe Failure Consequence (PFC) (Salehi et al 2018b, AWWA 2014.…”