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Engineering Director Inc.
Engineering Director Inc.

CGR Prediction Underground Pipeline Machine Learning

Machine Learning to Predict CGR Underground Pipeline

NACE Abstract
MP Magazine

How to cost-effectively and efficiently assess environmental conditions and the related impact of corrosion on underground pipeline using geographical information systems (GIS) and spatial data, with limited excavation. The objective is to proactively target those areas that have the highest likelihood of advanced corrosion (based on rate and degree of corrosion) and thus reduce risk of failure, while maximizing both capacity and related cost of inspection.