A probabilistic framework based on statistical learning theory for structural reliability analysis of transmission line systems

A probabilistic framework based on statistical learning theory for structural reliability analysis of transmission line systems. Rezaei, S. N., Chouinard, L., Langlois, S., & Legeron, F. Structure and Infrastructure Engineering, 13(12):1538 - 1552, 2017. Climatic hazards;Finite element solver;Performance functions;Probabilistic framework;Statistical learning theory;Statistical modeling;Structural reliability analysis;Transmission line systems;

Paper abstract bibtex

This paper describes a novel application of statistical learning theory to structural reliability analysis of transmission lines considering the uncertainties of climatic variables such as, wind speed, ice thickness and wind angle, and of the resistance of structural elements. The problem of reliability analysis of complex structural systems with implicit limit state functions is addressed by statistical model selection, where the goal is to select a surrogate model of the finite element solver that provides the value of the performance function for each conductor, insulator or tower element. After determining the performance function for each structural element, Monte Carlo simulation is used to calculate their failure probabilities. The failure probabilities of towers and the entire line are then estimated from the failure probabilities of their elements/components considering the correlation between failure events. In order to quantify the relative importance of line components and provide the engineers with a practical decision tool, the paper presents the calculation of two types of component importance measures. The presented methodology can be used to achieve optimised design, and to assess upgrading strategies to increase the line capacity.
© 2017 Informa UK Limited, trading as Taylor & Francis Group.

@article{20171103442437 ,
language = {English},
copyright = {Compilation and indexing terms, Copyright 2023 Elsevier Inc.},
copyright = {Compendex},
title = {A probabilistic framework based on statistical learning theory for structural reliability analysis of transmission line systems},
journal = {Structure and Infrastructure Engineering},
author = {Rezaei, Seyedeh Nasim and Chouinard, Luc and Langlois, Sebastien and Legeron, Frederic},
volume = {13},
number = {12},
year = {2017},
pages = {1538 - 1552},
issn = {15732479},
abstract = {This paper describes a novel application of statistical learning theory to structural reliability analysis of transmission lines considering the uncertainties of climatic variables such as, wind speed, ice thickness and wind angle, and of the resistance of structural elements. The problem of reliability analysis of complex structural systems with implicit limit state functions is addressed by statistical model selection, where the goal is to select a surrogate model of the finite element solver that provides the value of the performance function for each conductor, insulator or tower element. After determining the performance function for each structural element, Monte Carlo simulation is used to calculate their failure probabilities. The failure probabilities of towers and the entire line are then estimated from the failure probabilities of their elements/components considering the correlation between failure events. In order to quantify the relative importance of line components and provide the engineers with a practical decision tool, the paper presents the calculation of two types of component importance measures. The presented methodology can be used to achieve optimised design, and to assess upgrading strategies to increase the line capacity.<br/> &copy; 2017 Informa UK Limited, trading as Taylor & Francis Group.},
key = {Electric lines},
keywords = {Monte Carlo methods;Intelligent systems;Structural analysis;Finite element method;Reliability analysis;Reliability theory;Uncertainty analysis;Wind;},
note = {Climatic hazards;Finite element solver;Performance functions;Probabilistic framework;Statistical learning theory;Statistical modeling;Structural reliability analysis;Transmission line systems;},
URL = {http://dx.doi.org/10.1080/15732479.2017.1299771},
}

Downloads: 0

{"_id":"TQPLndx8SKw3hWMKA","bibbaseid":"rezaei-chouinard-langlois-legeron-aprobabilisticframeworkbasedonstatisticallearningtheoryforstructuralreliabilityanalysisoftransmissionlinesystems-2017","author_short":["Rezaei, S. N.","Chouinard, L.","Langlois, S.","Legeron, F."],"bibdata":{"bibtype":"article","type":"article","language":"English","copyright":"Compendex","title":"A probabilistic framework based on statistical learning theory for structural reliability analysis of transmission line systems","journal":"Structure and Infrastructure Engineering","author":[{"propositions":[],"lastnames":["Rezaei"],"firstnames":["Seyedeh","Nasim"],"suffixes":[]},{"propositions":[],"lastnames":["Chouinard"],"firstnames":["Luc"],"suffixes":[]},{"propositions":[],"lastnames":["Langlois"],"firstnames":["Sebastien"],"suffixes":[]},{"propositions":[],"lastnames":["Legeron"],"firstnames":["Frederic"],"suffixes":[]}],"volume":"13","number":"12","year":"2017","pages":"1538 - 1552","issn":"15732479","abstract":"This paper describes a novel application of statistical learning theory to structural reliability analysis of transmission lines considering the uncertainties of climatic variables such as, wind speed, ice thickness and wind angle, and of the resistance of structural elements. The problem of reliability analysis of complex structural systems with implicit limit state functions is addressed by statistical model selection, where the goal is to select a surrogate model of the finite element solver that provides the value of the performance function for each conductor, insulator or tower element. After determining the performance function for each structural element, Monte Carlo simulation is used to calculate their failure probabilities. The failure probabilities of towers and the entire line are then estimated from the failure probabilities of their elements/components considering the correlation between failure events. In order to quantify the relative importance of line components and provide the engineers with a practical decision tool, the paper presents the calculation of two types of component importance measures. The presented methodology can be used to achieve optimised design, and to assess upgrading strategies to increase the line capacity.<br/> © 2017 Informa UK Limited, trading as Taylor & Francis Group.","key":"20171103442437","keywords":"Monte Carlo methods;Intelligent systems;Structural analysis;Finite element method;Reliability analysis;Reliability theory;Uncertainty analysis;Wind;","note":"Climatic hazards;Finite element solver;Performance functions;Probabilistic framework;Statistical learning theory;Statistical modeling;Structural reliability analysis;Transmission line systems;","url":"http://dx.doi.org/10.1080/15732479.2017.1299771","bibtex":"@article{20171103442437 ,\nlanguage = {English},\ncopyright = {Compilation and indexing terms, Copyright 2023 Elsevier Inc.},\ncopyright = {Compendex},\ntitle = {A probabilistic framework based on statistical learning theory for structural reliability analysis of transmission line systems},\njournal = {Structure and Infrastructure Engineering},\nauthor = {Rezaei, Seyedeh Nasim and Chouinard, Luc and Langlois, Sebastien and Legeron, Frederic},\nvolume = {13},\nnumber = {12},\nyear = {2017},\npages = {1538 - 1552},\nissn = {15732479},\nabstract = {This paper describes a novel application of statistical learning theory to structural reliability analysis of transmission lines considering the uncertainties of climatic variables such as, wind speed, ice thickness and wind angle, and of the resistance of structural elements. The problem of reliability analysis of complex structural systems with implicit limit state functions is addressed by statistical model selection, where the goal is to select a surrogate model of the finite element solver that provides the value of the performance function for each conductor, insulator or tower element. After determining the performance function for each structural element, Monte Carlo simulation is used to calculate their failure probabilities. The failure probabilities of towers and the entire line are then estimated from the failure probabilities of their elements/components considering the correlation between failure events. In order to quantify the relative importance of line components and provide the engineers with a practical decision tool, the paper presents the calculation of two types of component importance measures. The presented methodology can be used to achieve optimised design, and to assess upgrading strategies to increase the line capacity.<br/> © 2017 Informa UK Limited, trading as Taylor & Francis Group.},\nkey = {Electric lines},\nkeywords = {Monte Carlo methods;Intelligent systems;Structural analysis;Finite element method;Reliability analysis;Reliability theory;Uncertainty analysis;Wind;},\nnote = {Climatic hazards;Finite element solver;Performance functions;Probabilistic framework;Statistical learning theory;Statistical modeling;Structural reliability analysis;Transmission line systems;},\nURL = {http://dx.doi.org/10.1080/15732479.2017.1299771},\n} \n\n\n","author_short":["Rezaei, S. N.","Chouinard, L.","Langlois, S.","Legeron, F."],"id":"20171103442437","bibbaseid":"rezaei-chouinard-langlois-legeron-aprobabilisticframeworkbasedonstatisticallearningtheoryforstructuralreliabilityanalysisoftransmissionlinesystems-2017","role":"author","urls":{"Paper":"http://dx.doi.org/10.1080/15732479.2017.1299771"},"keyword":["Monte Carlo methods;Intelligent systems;Structural analysis;Finite element method;Reliability analysis;Reliability theory;Uncertainty analysis;Wind;"],"metadata":{"authorlinks":{}}},"bibtype":"article","biburl":"https://bibbase.org/network/files/m3A3cns49MKmeRzt5","dataSources":["abFLQcuAzB7s4EL4a","iK69edT4peeXELcwY","6Aw5QAtT2yLJ3d2JP","qBPxBjrzghRQ4Wzbf","EnwwBMFxopKkypo2f","8uWbXNBCDiEboMj8h"],"keywords":["monte carlo methods;intelligent systems;structural analysis;finite element method;reliability analysis;reliability theory;uncertainty analysis;wind;"],"search_terms":["probabilistic","framework","based","statistical","learning","theory","structural","reliability","analysis","transmission","line","systems","rezaei","chouinard","langlois","legeron"],"title":"A probabilistic framework based on statistical learning theory for structural reliability analysis of transmission line systems","year":2017}