Global and local parameters for characterizing and modeling external corrosion in underground coated steel pipelines: A review of critical factors

Kim, Changkyu; Chen, Lin; Hui, Wang; Castaneda, Homero

doi:10.1016/j.jpse.2021.01.010

Cited by 49 publications

(15 citation statements)

References 119 publications

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“…3, pp has the strongest contribution with an importance above 30%, which indicates that this feature is extremely important for the dmax of the pipeline. The pp (protection potential, natural potential, E on or E off potential) is a parameter related to the size of the electrochemical half-cell and is an indirect parameter of the surface state of the pipe at a single location, which covers the macroscopic conditions during the assessment of the field conditions 31 . The industry generally considers steel pipes to be well protected at pp below −850 mV 32 .…”

Section: Adaboost Model Optimizationmentioning

confidence: 99%

Interpretable machine learning for maximum corrosion depth and influence factor analysis

et al. 2023

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We have employed interpretable methods to uncover the black-box model of the machine learning (ML) for predicting the maximum pitting depth (dmax) of oil and gas pipelines. Ensemble learning (EL) is found to have higher accuracy compared with several classical ML models, and the determination coefficient of the adaptive boosting (AdaBoost) model reaches 0.96 after optimizing the features and hyperparameters. In this work, the running framework of the model was clearly displayed by visualization tool, and Shapley Additive exPlanations (SHAP) values were used to visually interpret the model locally and globally to help understand the predictive logic and the contribution of features. Furthermore, the accumulated local effect (ALE) successfully explains how the features affect the corrosion depth and interact with one another.

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Section: Adaboost Model Optimizationmentioning

confidence: 99%

Interpretable machine learning for maximum corrosion depth and influence factor analysis

et al. 2023

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“…These activities, such as piping services for mining purposes, were potential contributors to Fe distribution in the environment. The corrosion factor from above and underground steel pipelines also transfers these elements into the water body (Kim et al, 2021 ). The other heavy metals might originate due to geological properties or human activities such as mining waste dumping and leachate from landfills (Eyankware & Obasi, 2021 ).…”

Section: Resultsmentioning

confidence: 99%

Mapping Chini Lake (Pahang, Malaysia) using Sentinel-2 images to determine the effect of acid mine drainage in the pre- to post-COVID-19 restriction period

Rendana

Idris

Rahim

2022

Environ Monit Assess

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Mining activities in the Chini Lake catchment area have been extensive for several years, contributing to acid mine drainage (AMD) events with high concentrations of iron (Fe) and other heavy metals impacting the surface water. However, during the restriction period due to the COVID-19 outbreak, anthropogenic activities have been suspended, which clearly shows a good opportunity for a better environment. Therefore, we aimed to analyze the variation of AMD-associated water pollution in three main zones of the Chini Lake catchment area using Sentinel-2 data for the periods pre-movement control order (MCO), during MCO, and post-MCO from 2019 to 2021. These three zones were chosen due to their proximity to mining areas: zone 1 in the northeastern part, zone 2 in the southeastern part, and zone 3 in the southern part of the Chini Lake area. The acid mine water index (AMWI) was a specific index used to estimate acid mine water. The AMWI values from Sentinel-2 images exhibited that the mean AMWI values in all zones during the MCO period decreased by 14% compared with the pre-MCO period. The spatiotemporal analysis found that the highest polluted zones were recorded in zone 1, followed by zone 3 and zone 2. As compared with during the MCO period, the maximum percentage of increment during post-MCO in all zones was up to 25%. The loosened restriction policy has resulted in more AMD flowing into surface water and increased pollution in Chini Lake. As a whole, our outputs revealed that Sentinel-2 data had a major potential for assessing the AMD-associated pollution of water.

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“…Perhaps, uncertainties can occur easily in realistic water pipe corrosion due to the complex nature of soils. Therefore, a non-deterministic (probabilistic) approach to soil corrosion modeling would be more appropriate and workable than the deterministic scheme [56].…”

Section: Probabilistic Approachmentioning

confidence: 99%

A proximate analysis of soil corrosivity to water pipelines in the Manohara Town Planning area of Kathmandu Valley using a probabilistic approach

Regmi¹,

Dahal

Bhattarai

2022

IOP Conf. Ser.: Mater. Sci. Eng.

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The facts of early non-performance of iron-based underground water pipes of the Kathmandu Valley (Nepal) are mostly due to the corrosive nature of the surrounding soil factors. A comparatively high percentage of the damage to the water pipelines in Nepal is anticipated, compared with the data reported in the developed countries. For the reasons, the quantitative data analysis of six kinds of the corrosive soil factors (i.e., moisture-holding capacity, pH, electrochemical resistivity, redox potential, Cl−, and SO4 2− ions) of 15 specimens, sampled from the Manohara Town Planning (MTP) area, was carried out using a novel probabilistic model, which is based on the internationally accepted standards (i.e., ASTM, NACE, AASTHO) with some modifications. The corrosivity of each soil factor was categorized into four corrosive groups (CGs) based on the conventionally applied classification methods. The experimentally obtained data of the soil factors were then used for proximate analysis using a new non-deterministic (probabilistic) model to classify the corrosive sub-groups (CSGs) of the soil specimens. This model would be a meritorious method to assess the soil corrosion condition of any area of the country, where the engineers and technologists could be applied pertinent methods or materials for the buried-pipe works in the future.

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Global and local parameters for characterizing and modeling external corrosion in underground coated steel pipelines: A review of critical factors

Cited by 49 publications

References 119 publications

Interpretable machine learning for maximum corrosion depth and influence factor analysis

Interpretable machine learning for maximum corrosion depth and influence factor analysis

Mapping Chini Lake (Pahang, Malaysia) using Sentinel-2 images to determine the effect of acid mine drainage in the pre- to post-COVID-19 restriction period

A proximate analysis of soil corrosivity to water pipelines in the Manohara Town Planning area of Kathmandu Valley using a probabilistic approach

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