The Future of Machine Learning on Corrosion How One Team is Automating the Industry: From Pipeline Corrosion and Beyond Rebecca A. Bickham Machine learning is something you encounter daily whether you realize it or not. From smart phones, apps, Siri and Alexa, online advertisements, and selfdriving cars, you potentially encounter artificial intelligence (AI) thousands of times per day. But did you ever consider how machine learning could influence the corrosion industry? Joseph Mazzella and Tom Hayden of Engineering Director, Inc. (EDI) (Evanston, Illinois, USA) are doing just that.

 “EDI is a consulting firm specializing in developing, implementing, measuring, and administrating lean business processes and strategies, through the effective use of information technology, AI, and geographical information systems [GIS],” says Mazzella, who is CEO of the company. “We have a keen focus on the corrosion industry.”

Description: This one-hour course provides insights and applications of Lean philosophies that impact contracts, collaboration, safety, productivity, and elimination of waste. The Akron Children’s Hospital project case-study is included and demonstrative of successful implementation; with a consistent focus on welfare and satisfaction of patients and their families. The Video is free to watch; there is a nominal charge of $30.00 to take the corresponding test and receive AIA CEU Credit online.

AIA CEU Learning module and video production by Engineering Director, Inc.

Estimating corrosion growth rate is a non-linear, multi-dimensional (space and time) challenge. Aboveground outdoor assets are affected by natural atmospheric factors such as climate, salinity and human contributions such as pollution. International Standard ISO9223 provides guidance, including response functions and a classification schema (C1 thru C5 and X) for estimating corrosion risk as a function of three variables: weather (temperature and humidity), dry deposition of sulfides, and dry deposition of chlorides.