Brittle Fracture Modeling for Steel Structures operated in the Extreme

Lepov, Valeriy; Grigoriev, Albert; Bisong, Mbelle Samuel; Lepova, Kyunna

doi:10.1016/j.prostr.2017.07.169

Cited by 6 publications

(3 citation statements)

References 10 publications

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“…Brittle fracture analysis was carried out for steel structures that were operated to the absolute limit by the structural modeling approach (FE and stochastic modeling) by Lepov et al [62]. It was concluded that to prevent the major amendment of the mechanical characteristics of welded constructions, a good non-destructive testing technique would be the microhardness control of the weld and HAZ.…”

Section: Numerical Approach-based Methodsmentioning

confidence: 99%

Advances in Machine Learning Techniques Used in Fatigue Life Prediction of Welded Structures

Gbagba,

Maccioni,

Concli

2023

Applied Sciences

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In the shipbuilding, construction, automotive, and aerospace industries, welding is still a crucial manufacturing process because it can be utilized to create massive, intricate structures with exact dimensional specifications. These kinds of structures are essential for urbanization considering they are used in applications such as tanks, ships, and bridges. However, one of the most important types of structural damage in welding continues to be fatigue. Therefore, it is necessary to take this phenomenon into account when designing and to assess it while a structure is in use. Although traditional methodologies including strain life, linear elastic fracture mechanics, and stress-based procedures are useful for diagnosing fatigue failures, these techniques are typically geometry restricted, require a lot of computing time, are not self-improving, and have limited automation capabilities. Meanwhile, following the conception of machine learning, which can swiftly discover failure trends, cut costs, and time while also paving the way for automation, many damage problems have shown promise in receiving exceptional solutions. This study seeks to provide a thorough overview of how algorithms of machine learning are utilized to forecast the life span of structures joined with welding. It will also go through their drawbacks and advantages. Specifically, the perspectives examined are from the views of the material type, application, welding method, input parameters, and output parameters. It is seen that input parameters such as arc voltage, welding speed, stress intensity factor range, crack growth parameters, stress histories, thickness, and nugget size influence output parameters in the manner of residual stress, number of cycles to failure, impact strength, and stress concentration factors, amongst others. Steel (including high strength steel and stainless steel) accounted for the highest frequency of material usage, while bridges were the most desired area of application. Meanwhile, the predominant taxonomy of machine learning was the random/hybrid-based type. Thus, the selection of the most appropriate and reliable algorithm for any requisite matter in this area could ultimately be determined, opening new research and development opportunities for automation, testing, structural integrity, structural health monitoring, and damage-tolerant design of welded structures.

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Section: Numerical Approach-based Methodsmentioning

confidence: 99%

Advances in Machine Learning Techniques Used in Fatigue Life Prediction of Welded Structures

Gbagba,

Maccioni,

Concli

2023

Applied Sciences

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“…Standards such as EN 1473 and NFPA 59A [14,15] or industrial standards of the major oil and gas companies stipulate that the steel structures being part of equipment, process units and piperacks related to processing/storing liquefied natural gas (LNG), shall be resistant to cryogenic exposure, i.e., to the impact of gas liquefied by compression, staying at ultralow (cryogenic) temperatures below −150 • C [16 -18], affecting to the destructive impact of the integrity of steel parts with embrittlement temperatures ranging from −20 • C to −40 • C, and structural connections, such as welded joints [19,20]. An LNG spill can cause a high-pressure heat load fire.…”

Section: Introductionmentioning

confidence: 99%

Fire Protection of Steel Structures with Epoxy Coatings under Cryogenic Exposure

2021

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Cases of fire with highly flammable, combustible liquids and combustible gases with high potential heat emission at oil and gas facilities are assumed to develop as a hydrocarbon fire, which is characterized by the temperature rising rapidly up to 1093 ± 56 °C within five minutes from the test start and staying within the same range throughout the test, as well as by overpressure being generated. Although various fireproof coating systems are commonly used to protect steel structures from high temperatures, a combination of fire protection and cryogenic spillage protection, i.e., protection from liquefied natural gas (LNG), is rather an international practice novelty regulated by standards ISO 20088. Thanks to their outstanding features, i.e., ability to sustain chemical and climatic impact, these epoxy-based materials are able to ensure positive fireproof performance for steel structures in the case of potential cryogenic impact. The article discusses tests on steel structures coated with epoxy fireproof compounds, specifically PREGRAD-EP, OGRAX-SKE and Chartek 2218. The test records show the time from the start of cryogenic exposure to the said sample reaching the limit state, as well as the time from the start of heat impact to the sample reaching the limit state in case of hydrocarbon fire temperature.

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“…Современная наука располагает средствами фрактографического анализа, экспериментального и численного моделирования, которые позволяют в полной мере понять механизмы, лежащие в основе процессов динамического разрушения материалов [1,2]. Однако в большинстве случаев численный расчет не заменяет аналитических решений, а последние могут пролить свет на истинную физическую природу разрушения, что позволяет учитывать структуру материала на различных масштабных уровнях с гораздо меньшими затратами времени и сил [3,4].…”

Section: Introductionunclassified

Physical Modeling of Brittle Crack Growth for dynamic impact on one-edge tension specimen

2019

ASNR

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Предложена модифицированная модель роста динамической трещины в вязкоупругом материале на основе структурного подхода, включающего термокинетическую концепцию С.Н. Журкова и критерий хрупкого разрушения Нейбера–Новожилова. Подход учитывает неоднородную микроструктуру материала и позволяет моделировать стадийность развития трещины, выявляемую фрактографическими исследованиями. Дан анализ основных параметров физически обоснованной модели, апробированной на данных эксперимента по импульсному расклиниванию образцов из полиметилметакрилата с острым боковым надрезом, нагружаемых статическим растяжением в широком диапазоне климатических температур.

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Brittle Fracture Modeling for Steel Structures operated in the Extreme

Cited by 6 publications

References 10 publications

Advances in Machine Learning Techniques Used in Fatigue Life Prediction of Welded Structures

Advances in Machine Learning Techniques Used in Fatigue Life Prediction of Welded Structures

Fire Protection of Steel Structures with Epoxy Coatings under Cryogenic Exposure

Physical Modeling of Brittle Crack Growth for dynamic impact on one-edge tension specimen

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