Pipe Inference

When data is incomplete, it often results in operations taking longer than planned as we make assumptions about the data we know is missing. Inferring your missing data will increase the completeness of your network, allowing you to better understand the total set of assets that you maintain and manage. Having confidence in a complete set of network data allows you to make better operational decisions and plan smarter for operations or incidents that will affect the network and your customers.

Sewer and pipe network data can be both missing, incomplete and inaccurate, causing increased risk to worker safety and impacting customer satisfaction. Inferring your data using our intelligent, rules-based solution allows you to view existing data and define what is missing. Automated actions based on the pipe network rules can be taken to correct existing data and create new data you know is missing. The rules allow you to get a confidence score against each inferred data set. With more reliable, accurate data, you can plan more efficiently, keep costs down and reduce the impact and disruption to customers and workers.

Pipe networks span large geographic areas and are often underground or difficult to access. Collecting data can be expensive, labour intensive and prone to error. Missing or incorrect data can increase costs associated with excavations and infrastructure damage, leading to unnecessary incidents that impact worker safety. Our solution improves the completeness of your existing data by inferring missing data and creating a complete network of your pipes. Taking an automated approach helps to improve operational efficiencies, gives you confidence in your data, and reduces the time and costs associated with inaccurate planning and unnecessary fines.

Your existing dataset combined with our tools and expertise, will create a digital twin of your pipe network which is underpinned by high quality and complete data. Improving the quality of your data will help simplify operational tasks and increase productivity. Having access to a digital twin of your network will allow you to move to a predictive or preventative approach to network management.

In 2011, the government transferred ownership of all private drains and sewers in England to local water companies. As a result, water companies assumed responsibility for additional wastewater pipes. Information on this historic infrastructure, however, is extremely limited. Although very little information is available about the transferred assets, water companies need to understand their network. 1Spatial’s winning approach is to automatically apply expert knowledge to the available information to infer the missing pipes and their characteristics.