Using AI and Machine Learning for Fast, Accurate and Affordable Pipeline Condition Assessments

This event was recorded Fri, Oct 12, 2018 10:00 AM CDT{LOCAL_TZ} and is now available for on demand viewing.

A certificate of attendance will be offered.

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Event Description:

Artificial Intelligence (AI), specifically, Machine Learning, has emerged as a technology to make a significant impact in buried water infrastructure asset management. It provides a large-scale comparison of myriad factors to generate a more refined and accurate prediction of condition based on the disparate interactions between component variables.  Machine Learning consumes large, complex data sets containing more variables humans can process with current tools. This objective, data-driven method overcomes inherent subjectivity and biases and provides results that help utilities make better replacement decisions.   Moreover, the availability of digital asset data from GIS, CMMS, and EAM systems enables a machine learning solution for pipe condition assessment to be fast, accurate and affordable.

Incorporating an Artificial Intelligence and Machine Learning Condition Assessment tool into a proper infrastructure and asset management program will contribute to the reduction of the economic impacts incurred from water main breaks, and more efficient allocation of capital by water utilities. Use of best practices and a more accurate, objective tool will align maintenance and capital repair and replacement strategies to more efficiently leverage scarce financial and human resources.  They also inject financial integrity to the planning process and refine the investment strategy so a utility will be in a better position to defend planning efforts and fund needed capital pipe replacement projects.

Presented by:

Adam Smith
Water Utility Manager
Menasha Utilities

Greg Baird
The Water Finance Research Foundation/Group

Doug Hatler
Chief Revenue Officer

Lars Stenstedt
Co-founder and COO

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