Tree-based machine learning models for predicting the maximum depth of corrosion defects based on historical in-line inspection data
Oil and gas pipelines are the primary means of fluid transportation in the industry due to their efficiency, reliability, and cost-effectiveness. However, pipeline corrosion poses significant risks, leading to loss of containment, operational interruptions, and potential loss of life if undetected o...
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| Principais autores: | , , , |
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| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
KeAi Communications Co. Ltd.
2026-01-01
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| Colecção: | Journal of Pipeline Science and Engineering |
| Assuntos: | |
| Acesso em linha: | http://www.sciencedirect.com/science/article/pii/S2667143325000551 |
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