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Mapping Indication Severity Using Bayesian Machine Learning From Indirect Inspection Data By Considering The Impact Of Soil Corrosivity

Product Number: 51321-16749-SG
Author: Hui Wang; Sreelakshmi Sreeharan; Homero Castaneda
Publication Date: 2021
$0.00
$20.00
$20.00

This paper provides the industry with a data-driven approach that can characterize the heterogeneity of the soil corrosivity along the pipeline right-of-way by integrating data from soil survey, indirect inspection technologies, in-line inspection, and a slightly broader database of environmental (precipitation and vegetation) data. The outcome is the spatial distribution of different soil corrosivity regions, and the statistical description of the corresponding corrosion factors, corrosion defect depth, and number of defects corresponding to each corrosivity region, which can be used as a basis for excavation schedule and related risk assessment. This proposed approach considers the statistical similarity and spatial variation of soil corrosivity at different locations along the pipeline right-of-way. The proposed method is implemented and demonstrated using a database of a 110km pipeline section.

This paper provides the industry with a data-driven approach that can characterize the heterogeneity of the soil corrosivity along the pipeline right-of-way by integrating data from soil survey, indirect inspection technologies, in-line inspection, and a slightly broader database of environmental (precipitation and vegetation) data. The outcome is the spatial distribution of different soil corrosivity regions, and the statistical description of the corresponding corrosion factors, corrosion defect depth, and number of defects corresponding to each corrosivity region, which can be used as a basis for excavation schedule and related risk assessment. This proposed approach considers the statistical similarity and spatial variation of soil corrosivity at different locations along the pipeline right-of-way. The proposed method is implemented and demonstrated using a database of a 110km pipeline section.

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