Effect of pitting corrosion on the ultimate strength of steel plates subjected to in-plane compression and bending

Nakai, Tatsuro; Matsushita, Hisao; Yamamoto, Norio

doi:10.1007/s00773-005-0203-4

Cited by 105 publications

(47 citation statements)

References 18 publications

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“…Value of the equivalent thickness is almost independent of the loading mode: from pure compression, through different combinations of compression and bending, up to pure bending [5]. Nakai and co-workers have compared these values of equivalent thickness to the values evaluated by tensile test, where the following proportion is valid: t e /t 0 = σ u / σ u0 (1) where t 0 is the original plate thickness, σ u and σ u0 are the nominal UTS for the plate with and without pits, respectively.…”

Section: Ultimate Strength Of Structural Modelsmentioning

confidence: 94%

“…Lateral-torsional buckling of whole hold frames, and local buckling and fracture of web and face plates in the lower parts of hold frames, is damage typically seen in the hold frames of bulk carriers [5]. Therefore extensive program of FE analyses has been realized [5] with square plates containing conical corrosion pits (aspect ratio depth-to-width of pit equal to 0.125) of different size and distribution.…”

Section: Ultimate Strength Of Structural Modelsmentioning

confidence: 99%

“…Therefore extensive program of FE analyses has been realized [5] with square plates containing conical corrosion pits (aspect ratio depth-to-width of pit equal to 0.125) of different size and distribution. Compressive load plus bending moment have been applied in different proportions: from pure compression (stress gradient equal 0) to pure bending (gradient equal 2).…”

Section: Ultimate Strength Of Structural Modelsmentioning

confidence: 99%

“…Thus a prediction of the strength of pitted plates using the average thickness loss would lead to non-conservative results. However, the average thickness loss or maximum pit depths are not always dominant factors for ultimate strength but the strength depends also on type of pit distribution on the both sides of the plate [5,9].…”

Section: Ultimate Strength Of Structural Modelsmentioning

confidence: 99%

“…Finite Element (FE) analyses of pitted square plates under uniaxial compression, biaxial compression, shear and combinations if the above mentioned loadings showed that the equivalent thickness can conservatively be predicted by equation (3) and the following equation [10]: t el = 1.25 · t al (5) Thus ultimate strength of pitted plate of an average thickness (t ave ') is lower than the strength of uniformly corroded plate of the same thickness. This means that unevenness of the plate surface due to pitting corrosion reduce ultimate strength of the plate.…”

Section: Fig 2 Tensile Strength Of Pitted Steel Versus Thickness Lomentioning

confidence: 99%

See 4 more Smart Citations

Influence of pitting corrosion on strength of steel ships and offshore structures

Jakubowski

2011

Polish Maritime Research

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Section: Ultimate Strength Of Structural Modelsmentioning

confidence: 94%

Section: Ultimate Strength Of Structural Modelsmentioning

confidence: 99%

Section: Ultimate Strength Of Structural Modelsmentioning

confidence: 99%

Section: Ultimate Strength Of Structural Modelsmentioning

confidence: 99%

Section: Fig 2 Tensile Strength Of Pitted Steel Versus Thickness Lomentioning

confidence: 99%

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Influence of pitting corrosion on strength of steel ships and offshore structures

Jakubowski

2011

Polish Maritime Research

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Classification of pitting corrosion damage in process facilities using supervised machine learning

Patel,

Aryai,

Arzaghi

et al. 2024

Can J Chem Eng

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Corrosion is widely known to be a major cause of the failures in process facilities. Prediction of corrosion damage is therefore essential for industries to manage the availability of their assets. This research aims to investigate the application of supervised machine learning methods for the classification of pitting corrosion damage. Several machine learning classifiers, namely ensemble methods, support vector machine (SVM), K‐nearest neighbours, and the decision tree are used to classify the extent of pitting corrosion damage in corroded steel samples. To simulate the corrosion of the steel samples, a series of laboratory experiments were conducted. After processing the results using appropriate statistical methods, the corrosion data was used to train the machine learning models. The trained models can predict the class of corrosion damage with acceptable accuracy using the material and environmental specifications of the samples. Additionally, a discussion on the selection of machine learning techniques which classify corrosion damage using a risk‐based approach is provided. With their optimal accuracy and lower risk of misclassification, the SVM and AdaBoost models perform better than the other studied models.

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Deterioration of marine offshore structures and subsea installations subjected to severely corrosive environment: A review

Xia,

Jia,

Garbatov

2024

Materials & Corrosion

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Corrosion degradation significantly contributes to the deterioration of offshore structures and subsea installations, impacting the durability of technology equipment while potentially leading to structural failure and environmental pollution. This review primarily focuses on the various types of corrosion observed in offshore structures, the factors influencing corrosion, and the resulting degradation of mechanical properties following corrosion exposure. The study examines the deterioration patterns in tensile properties of offshore structures and subsea facilities, along with the assessment and analysis of corrosion in offshore steel bridges and submarine pipelines. Future research should include a comprehensive scientific investigation of corrosion mechanisms and the development of engineering predictive models to assess corrosion failure and extend the remaining life of offshore structures.

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Effect of pitting corrosion on the ultimate strength of steel plates subjected to in-plane compression and bending

Cited by 105 publications

References 18 publications

Influence of pitting corrosion on strength of steel ships and offshore structures

Influence of pitting corrosion on strength of steel ships and offshore structures

Classification of pitting corrosion damage in process facilities using supervised machine learning

Deterioration of marine offshore structures and subsea installations subjected to severely corrosive environment: A review

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