{"id":69235,"date":"2023-01-26T13:57:25","date_gmt":"2023-01-26T12:57:25","guid":{"rendered":"https:\/\/www.kwrwater.nl\/actueel\/het-ongeziene-goed-beheren-met-effectief-datamanagement\/"},"modified":"2023-02-01T08:49:24","modified_gmt":"2023-02-01T07:49:24","slug":"using-data-effectively-for-the-sound-management-of-concealed-assets","status":"publish","type":"post","link":"https:\/\/www.kwrwater.nl\/en\/actueel\/using-data-effectively-for-the-sound-management-of-concealed-assets\/","title":{"rendered":"Using data effectively for the sound management of concealed assets"},"content":{"rendered":"
\n

As the mains get older, monitoring the condition of underground pipes is increasingly challenging for drinking water companies and water authorities. The Midas TKI project has taken the first step towards enabling end users to make more educated and accurate assessments of the failure probabilities of their pipes. That will contribute to better decisions about whether or not to upgrade the mains. \u201cThe main benefit is that this study shows how important it is to collect data properly and reliably,\u201d says Jurjen den Besten of data science consultancy Spatial Insight.<\/p>\n<\/div>\n

The goal of the Midas (Multiple Data Sources) project<\/a> was to improve data for use in pipe condition models. The research focus was on the interplay between individual data components and their influence on the margin of uncertainty in predictions. Three technology suppliers were closely involved in the project. HDM Pipelines<\/a> \u2013 an operator and asset manager of underground infrastructure \u2013 played an important role in connecting research, practice and services. Acquaint<\/a> \u2013 a company that inspects underground pipes \u2013 supplied and prepared non-public data and services. And Spatial Insight<\/a> was responsible for integrating data about pipe quality, and quality control for those data. KWR managed the research component of the project.<\/p>\n

\u201cThe main benefit is that this study shows how important it is to collect data properly and reliably.\u201d<\/p><\/blockquote>\n

Data science<\/h2>\n

For Spatial Insight \u2013 a company that helps drinking water companies to manage underground assets optimally \u2013 the goal of the project provided an interesting way to approach this area, explains Den Besten. \u201cData science allows us to use knowledge about malfunctions in the past to anticipate malfunctions in the future. Midas had a slightly different focus. The idea wasn\u2019t to predict malfunctions but to determine the quality of pipes without having to dig them up or inspect them. As well as drinking water companies, water authorities are also increasingly interested in data science. If we could predict pipe quality for the whole of the Netherlands, and then compare that information with actual malfunctions, we could superimpose these two categories of data to identify overlaps. That would deliver enormous added value for asset management.\u201d<\/p>\n

Complex puzzle<\/h2>\n

Den Besten emphasises how important it is to organise data integration \u2013 tying different data sources together \u2013 in the right way. \u201cYou can try to conduct analyses, but if the quality of the underlying data isn\u2019t good enough, that won\u2019t work.\u201d But even if data organisation is good, predicting what happens underground is a complex puzzle, Den Besten explains. He blames this on the inadequate documentation of the biography of the mains. The construction work in the 1960s \u2013 with intense time pressures because of the acute housing shortage \u2013 was an excellent achievement. But there are no data about changes in the condition of the pipes after construction was completed. So there are extensive gaps in our knowledge about the factors that affect the life cycle of pipes. The Midas project looked at whether the data that are available can be used to predict the condition of pipes.<\/p>\n

Limited correlation<\/h2>\n

Den Besten explains. \u201cIf you know how parameters are related to measurements of the pipe condition at one location, you can use those parameters at another location to predict the condition of pipes without making actual measurements. For example, if a pipe is very thin somewhere, and you also find acidic groundwater at that location, you can surmise that there is a connection. So you will expect wall thickness to be affected in other places with acidic groundwater. But things aren\u2019t that simple, unfortunately. This study has shown that the correlation between known parameters such as groundwater acidity, and target variables such as wall thickness, is actually limited. This may be due to differences in pipe quality, for example. However, as we have already seen, the data aren\u2019t available. We simply don\u2019t know what we don\u2019t know. That\u2019s quite often a problem.\u2019<\/p>\n

\u201cThe dashboard is a support tool that helps us to identify the right focus for the time ahead.\u201d<\/p><\/blockquote>\n

Dashboard<\/h2>\n

Nevertheless, enough data were collected to allow Midas to make progress on the desired integration. A concrete result emerged: a dashboard developed by KWR<\/a> for inputting variables in order to determine their effect on the condition and performance of the pipe. End user George Galama of the Zuiderzeeland water authority is enthusiastic about this practical tool. \u201cSince 2016, we have been taking an active approach to designing the management of our pipelines. We got on board with this project with the aim of being involved with new developments in this field. The dashboard is a support tool that helps us to identify the right focus for the time ahead. We have acquired a picture of which data we collect are accurate enough, and where we still have to do our homework. Moreover, we can use both the dashboard and the report \u2013 with a specific chapter for end users \u2013 to initiate discussions with management. It is often difficult to get people thinking about subsurface issues so that funds are earmarked for this purpose. Obviously, the dashboard isn\u2019t yet an off-the-shelf end product that tells us which investments are needed, or where. But it\u2019s certainly a useful addition to our toolbox.\u201d<\/p>\n

Balancing act<\/h2>\n

Den Besten says that the dashboard is primarily a useful instrument that shows the extent to which the variation in source data affects the final outcome. He describes it as an \u2018awareness instrument\u2019 that teaches end users about how data uncertainty in successive stages can have a knock-on effect. \u201cIt makes you realise how important it is to collect high-quality data. Even though it remains to be seen whether we will achieve reliable predictions of pipe condition with data integration. As I said, we have found that it is very difficult to make a good estimate based on data. Here at Spatial Insight, because using data about pipe condition to predict leaks is so difficult, we primarily base our calculations on historical leaks. These data are recorded very well by the Dutch drinking water companies. Sometimes, they opt for malfunction-dependent maintenance but that is a very delicate issue in the public arena, and rightly so. If, on the other hand, you opt for preventive replacement, you have to accept the need to invest money and resources to ensure that all the pipes are safe. That trade-off between economic value and public perception can be quite a balancing act. In general, it comes down to the question of the best way to tackle the challenge of ageing assets.\u201d<\/p>\n

\u201c \u2026 we were given enough opportunities to describe where we had questions, and then steps were taken accordingly.\u201d<\/p><\/blockquote>\n

Theory to practice<\/h2>\n

Galama of the Zuiderzeeland water authority is very happy with the alliance with Spatial Insight, Acquaint, HDM Pipelines and KWR. Alongside the drinking water companies, he was involved as an end user in the work that has been done in the meantime. \u201cThe study was very theoretical,\u201d he says. \u201cBut the switch to practice was done well. We were given enough opportunities to describe where we had questions, and then steps were taken accordingly.\u201d When asked how to implement the next step in practice, Den Besten\u2019s advice is clear. \u201cEstablish a sound and structured approach to asset data. And during that process, don\u2019t forget about the basic layer of information, with data about the diameter, material and year of construction of pipes, because these data are precisely the data that are extremely valuable.\u201d<\/p>\n

TKI as a valuable environment<\/h2>\n

From the technology suppliers\u2019 point of view, Den Besten agrees that Midas has provided enough ammunition for them to continue working on deploying pipe condition models to interpret locality data and the inspections made by their clients in the years ahead. He does, however, have a reservation to make. \u201cThe project is a good place to start in terms of being aware that we don\u2019t know everything. And I have my doubts about whether it is possible to acquire the information we don\u2019t have at the moment. It is precisely for this type of research that TKI is a valuable environment. It gives you the room to identify gaps in knowledge. These studies can sometime be seen as failures. But this kind of research is actually useful in terms of deciding where to go next. That\u2019s something we can be proud of.\u201d<\/p>\n

The MIDAS project involved the following partners:\u00a0Acquaint, Brabant Water, HDM Pipelines, KWR, Spatial Insight, and the Limburg and Zuiderzeeland water authorities.<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"

As the mains get older, monitoring the condition of underground pipes is increasingly challenging for drinking water companies and water authorities. 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