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Bayesian probabilistic inference for nonparametric damage detection of structures / Xiaomo Jiang in Journal of engineering mechanics, Vol. 134 N°10 (Octobre 2008) 

Public ISBD [article]  in Journal of engineering mechanics > Vol. 134 N°10 (Octobre 2008) . - pp.820–831. Titre : Bayesian probabilistic inference for nonparametric damage detection of structures Type de document : texte imprimé Auteurs : Xiaomo Jiang, Auteur ; Sankaran Mahadevan, Auteur Année de publication : 2008 Article en page(s) : pp.820–831. Note générale : Mécanique appliquée Langues : Anglais (eng) Mots-clés : Bayesian  analysis  Damage  assessment  Structural  safety  Bench  marks  Identification  Probability  Parameters Résumé : This paper presents a Bayesian hypothesis testing-based probabilistic assessment method for nonparametric damage detection of building structures, considering the uncertainties in both experimental results and model prediction. A dynamic fuzzy wavelet neural network method is employed as a nonparametric system identification model to predict the structural responses for damage evaluation. A Bayes factor evaluation metric is derived based on Bayes’ theorem and Gaussian distribution assumption of the difference between the experimental data and model prediction. The metric provides quantitative measure for assessing the accuracy of system identification and the state of global health of structures. The probability density function of the Bayes factor is constructed using the statistics of the difference of response quantities and Monte Carlo simulation technique to address the uncertainties in both experimental data and model prediction. The methodology is investigated with five damage scenarios of a four-story benchmark building. Numerical results demonstrate that the proposed methodology provides an effective approach for quantifying the damage confidence in the structural condition assessment. ISSN : 0733-9399 En ligne : http://ascelibrary.org/doi/abs/10.1061/%28ASCE%290733-9399%282008%29134%3A10%288 [...] [article] Bayesian probabilistic inference for nonparametric damage detection of structures [texte imprimé] / Xiaomo Jiang, Auteur ; Sankaran Mahadevan, Auteur . - 2008 . - pp.820–831. Mécanique appliquée Langues : Anglais (eng) in Journal of engineering mechanics > Vol. 134 N°10 (Octobre 2008) . - pp.820–831. Mots-clés : Bayesian  analysis  Damage  assessment  Structural  safety  Bench  marks  Identification  Probability  Parameters Résumé : This paper presents a Bayesian hypothesis testing-based probabilistic assessment method for nonparametric damage detection of building structures, considering the uncertainties in both experimental results and model prediction. A dynamic fuzzy wavelet neural network method is employed as a nonparametric system identification model to predict the structural responses for damage evaluation. A Bayes factor evaluation metric is derived based on Bayes’ theorem and Gaussian distribution assumption of the difference between the experimental data and model prediction. The metric provides quantitative measure for assessing the accuracy of system identification and the state of global health of structures. The probability density function of the Bayes factor is constructed using the statistics of the difference of response quantities and Monte Carlo simulation technique to address the uncertainties in both experimental data and model prediction. The methodology is investigated with five damage scenarios of a four-story benchmark building. Numerical results demonstrate that the proposed methodology provides an effective approach for quantifying the damage confidence in the structural condition assessment. ISSN : 0733-9399 En ligne : http://ascelibrary.org/doi/abs/10.1061/%28ASCE%290733-9399%282008%29134%3A10%288 [...]