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    Intelligent Code for Assessing Performance and Reliability of Failure Assessment Models

    Source: Journal of Pipeline Systems Engineering and Practice:;2025:;Volume ( 016 ):;issue: 002::page 04025003-1
    Author:
    Guo Lingyun
    ,
    Wang Hexian
    ,
    Chen Bo
    ,
    Niffenegger Markus
    DOI: 10.1061/JPSEA2.PSENG-1755
    Publisher: American Society of Civil Engineers
    Abstract: Pipelines often fail due to structural degradation and environmental factors, impacting the accuracy of failure assessment models (FAMs) such as Structural Integrity Assessment Procedures for European Industry (SINTAP), R6, and BS7910. Traditional error analysis methods are limited by their complexity and lack of practical application. This paper introduces an Intelligent Code for Evaluating Models (ICEM) to accurately assess FAM prediction accuracy. ICEM uses an experimental database to compute characteristic parameters and distribution patterns of prediction accuracy. It comprises four components: feature parameter extractor (FPE), fitted distribution processor (FDP), overfitting processor (OP), and recommendation factor processor (RFP). FPE calculates moment parameters like mean, median, standard deviation, and coefficient of variation. FDP identifies the best-fit distribution using the maximum likelihood method, EM algorithms, and goodness-of-fit (GoF) tests (K-S test, NlogL, AIC, and BIC). OP prevents overfitting by removing outliers, and RFP provides a recommended factor (rf) based on the best-fit distributions and specific project risk degrees. By offering a systematic and practical approach, ICEM enhances the reliability of FAMs, thereby significantly improving prediction confidence and accuracy, ultimately contributing to better pipeline management, safety, and operational efficiency.
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      Intelligent Code for Assessing Performance and Reliability of Failure Assessment Models

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4304811
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    contributor authorGuo Lingyun
    contributor authorWang Hexian
    contributor authorChen Bo
    contributor authorNiffenegger Markus
    date accessioned2025-04-20T10:29:00Z
    date available2025-04-20T10:29:00Z
    date copyright1/23/2025 12:00:00 AM
    date issued2025
    identifier otherJPSEA2.PSENG-1755.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4304811
    description abstractPipelines often fail due to structural degradation and environmental factors, impacting the accuracy of failure assessment models (FAMs) such as Structural Integrity Assessment Procedures for European Industry (SINTAP), R6, and BS7910. Traditional error analysis methods are limited by their complexity and lack of practical application. This paper introduces an Intelligent Code for Evaluating Models (ICEM) to accurately assess FAM prediction accuracy. ICEM uses an experimental database to compute characteristic parameters and distribution patterns of prediction accuracy. It comprises four components: feature parameter extractor (FPE), fitted distribution processor (FDP), overfitting processor (OP), and recommendation factor processor (RFP). FPE calculates moment parameters like mean, median, standard deviation, and coefficient of variation. FDP identifies the best-fit distribution using the maximum likelihood method, EM algorithms, and goodness-of-fit (GoF) tests (K-S test, NlogL, AIC, and BIC). OP prevents overfitting by removing outliers, and RFP provides a recommended factor (rf) based on the best-fit distributions and specific project risk degrees. By offering a systematic and practical approach, ICEM enhances the reliability of FAMs, thereby significantly improving prediction confidence and accuracy, ultimately contributing to better pipeline management, safety, and operational efficiency.
    publisherAmerican Society of Civil Engineers
    titleIntelligent Code for Assessing Performance and Reliability of Failure Assessment Models
    typeJournal Article
    journal volume16
    journal issue2
    journal titleJournal of Pipeline Systems Engineering and Practice
    identifier doi10.1061/JPSEA2.PSENG-1755
    journal fristpage04025003-1
    journal lastpage04025003-13
    page13
    treeJournal of Pipeline Systems Engineering and Practice:;2025:;Volume ( 016 ):;issue: 002
    contenttypeFulltext
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