Using Artificial Intelligence for Water Pipeline Infrastructure Asset Management

It is critical for society that we transform our siloed water management and infrastructure systems into smart, connected, sustainable, and resilient systems. This transformation can help us to address the effects of increasing extreme climate events, ecosystem demands, rapid global urbanization, and infrastructure deterioration from age and neglect. As water utilities improve their asset management programs, it is imperative that the data-driven decision support systems represent the complexities within water pipeline infrastructure systems. Artificial intelligence (AI) techniques can enable modelers to train mathematical algorithms to learn complex patterns from data and represent the water pipeline infrastructure systems accurately. PIPEiD is a national database platform that uses artificial intelligence (AI) and machine learning techniques to assess the performance and risk of water pipelines to help utilities better assess pipe replacement decisions and allocate funding. PIPEiD (Pipeline Infrastructure Database) will assist water sector utilities to manage water pipeline infrastructure systems more effectively for performance, resiliency, and sustainability. PIPEiD will provide the secure, robust, and centralized web-based database platform to address all three major infrastructure asset management levels: strategic, tactical, and operational for utilities of all sizes (small, medium, and large) across the country. The research team collected field performance data for potable, raw, and reuse water pipelines made from materials reflecting the wide range of pipes currently in the ground throughout the US, including cast and ductile iron, prestressed concrete cylinder pipe, reinforced concrete, steel, thermoplastic, PVC, and asbestos. The researchers worked to collect data distributed across different ecological areas, or cohorts, organized based on the climatic conditions of the 500 water utilities’ locations. These cohorts included coastal, arid, Arctic, and mountainous regions. Researchers factored in environmental conditions, such as soil corrosivity, traffic loading, and frost action, that can affect pipelines. The team enhanced the data set out further with the help of external sources like the United States Geological Survey (USGS), the Soil Survey Geographic Database from the United States Department of Agriculture (USDA), and additional field data collected by a group of 25 water utilities across the country that were selected to validate the models and tools. This paper will present various applications of artificial intelligence and machine learning algorithms for advanced asset management of water pipeline infrastructure systems.