Deakin University
Browse

File(s) under permanent embargo

A BIM-based approach for predicting corrosion under insulation

Version 2 2024-06-06, 08:47
Version 1 2019-08-20, 08:06
journal contribution
posted on 2024-06-06, 08:47 authored by YH Tsai, Jun WangJun Wang, WT Chien, CY Wei, X Wang, SH Hsieh
Corrosion under insulation is one of the most important issues in the petroleum industry. Ordinarily, in order to check the corrosion, inspectors remove the insulation of pipelines to measure the level of corrosion on each section of pipelines. This procedure may take weeks for a site which distinctly affects the financial aspect of oil and gas companies due to the pause production of its high-value products; therefore, in most cases, inspectors spot-check pipeline corrosion based on their experience. However, because the environments on sites are various, experience-based inspection may not be suitable for every site. On the other hand, even though inspectors want to access more data for better understanding of the site before the site trip, historical data sometimes are lost or scattered which leads to a hard situation for preparation of corrosion inspection. This paper utilises passive RFID sensors, which are smart sensing technologies, to collect site data and then integrate them into a Building Information Modeling (BIM) system. A uniform corrosion model is also adapted from the theories of corrosion to leverage both sensor data and BIM elements' properties. They serve as inputs to calculate the corrosion rate which is the key value of corrosion prediction. Then, the corrosion prediction results are colour-coded on a BIM model which helps inspectors intuitively understand the prediction and prepare for the site inspection. In result, the proposed research could provide a novel approach for corrosion management under insulation.

History

Journal

Automation in construction

Volume

107

Article number

102923

Pagination

1-14

Location

Amsterdam, The Netherlands

ISSN

0926-5805

Language

eng

Publication classification

C1 Refereed article in a scholarly journal

Copyright notice

2019, Elsevier B.V.

Publisher

Elsevier

Usage metrics

    Research Publications

    Categories

    No categories selected

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC