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Modeling of stress spectrum using long - term monitoring data and finite mixture distributions / Y.Q. Ni in Journal of engineering mechanics, Vol. 138 N° 2 (Fevrier 2012) 

Public ISBD [article]  in Journal of engineering mechanics > Vol. 138 N° 2 (Fevrier 2012) . - pp.175-183 Titre : Modeling of stress spectrum using long - term monitoring data and finite mixture distributions Type de document : texte imprimé Auteurs : Y.Q. Ni, Auteur ; X. W. Ye, Auteur ; J. M. Ko, Auteur Année de publication : 2012 Article en page(s) : pp.175-183 Note générale : Mécanique appliquée Langues : Anglais (eng) Mots-clés : Fatigue  Monitoring  Stress  Probability  density  functions  Probability  distribution  Steel  bridges  Suspension  bridges Résumé : This study focuses on how to exploit long-term monitoring data of structural strain for analytical modeling of multimodal rainflow-counted stress spectra by use of the method of finite mixture distributions in conjunction with a hybrid mixture parameter estimation algorithm. The long-term strain data acquired from an instrumented bridge carrying both highway and railway traffic is used to verify the procedure. A wavelet-based filtering technique is first applied to eliminate the temperature effect inherent in the measured strain data. The stress spectrum is obtained by extracting the stress range and mean stress from the stress time histories with the aid of a rainflow counting algorithm. By synthesizing the features captured from daily stress spectra, a representative sample of stress spectrum accounting for multiple loading effects is derived. Then, the modeling of the multimodal stress range is performed by use of finite mixed normal, lognormal, and Weibull distributions, with the best mixed distribution being determined by the Akaike’s information criterion (AIC). The joint probability density function (PDF) of the stress range and the mean stress is also estimated by means of a mixture of multivariate distributions. It turns out that the obtained PDFs favorably fit the measurement data and reflect the multimodal property fairly well. The analytical expressions of PDFs resulting from this study would greatly facilitate the monitoring-based fatigue reliability assessment of steel bridges instrumented with structural health monitoring (SHM) system. Note de contenu : Article Outlin 1. Introduction 2. Finite Mixture Distributions 1. Structure of Finite Mixture Distributions 2. Hybrid Mixture Parameter Estimation 3. Derivation of Monitoring-Based Stress Spectrum 1. Monitoring Data of Dynamic Strain 2. Wavelet Processing of Measured Dynamic Strain 3. Representative Sample of Stress Spectrum 4. Modeling of Stress Spectrum 1. Multimodal PDF of Stress Range 2. Modeling of Stress Matrix 5. Conclusion ISSN : 0733-9399 En ligne : http://ascelibrary.org/emo/resource/1/jenmdt/v138/i2/p175_s1?isAuthorized=no [article] Modeling of stress spectrum using long - term monitoring data and finite mixture distributions [texte imprimé] / Y.Q. Ni, Auteur ; X. W. Ye, Auteur ; J. M. Ko, Auteur . - 2012 . - pp.175-183. Mécanique appliquée Langues : Anglais (eng) in Journal of engineering mechanics > Vol. 138 N° 2 (Fevrier 2012) . - pp.175-183 Mots-clés : Fatigue  Monitoring  Stress  Probability  density  functions  Probability  distribution  Steel  bridges  Suspension  bridges Résumé : This study focuses on how to exploit long-term monitoring data of structural strain for analytical modeling of multimodal rainflow-counted stress spectra by use of the method of finite mixture distributions in conjunction with a hybrid mixture parameter estimation algorithm. The long-term strain data acquired from an instrumented bridge carrying both highway and railway traffic is used to verify the procedure. A wavelet-based filtering technique is first applied to eliminate the temperature effect inherent in the measured strain data. The stress spectrum is obtained by extracting the stress range and mean stress from the stress time histories with the aid of a rainflow counting algorithm. By synthesizing the features captured from daily stress spectra, a representative sample of stress spectrum accounting for multiple loading effects is derived. Then, the modeling of the multimodal stress range is performed by use of finite mixed normal, lognormal, and Weibull distributions, with the best mixed distribution being determined by the Akaike’s information criterion (AIC). The joint probability density function (PDF) of the stress range and the mean stress is also estimated by means of a mixture of multivariate distributions. It turns out that the obtained PDFs favorably fit the measurement data and reflect the multimodal property fairly well. The analytical expressions of PDFs resulting from this study would greatly facilitate the monitoring-based fatigue reliability assessment of steel bridges instrumented with structural health monitoring (SHM) system. Note de contenu : Article Outlin 1. Introduction 2. Finite Mixture Distributions 1. Structure of Finite Mixture Distributions 2. Hybrid Mixture Parameter Estimation 3. Derivation of Monitoring-Based Stress Spectrum 1. Monitoring Data of Dynamic Strain 2. Wavelet Processing of Measured Dynamic Strain 3. Representative Sample of Stress Spectrum 4. Modeling of Stress Spectrum 1. Multimodal PDF of Stress Range 2. Modeling of Stress Matrix 5. Conclusion ISSN : 0733-9399 En ligne : http://ascelibrary.org/emo/resource/1/jenmdt/v138/i2/p175_s1?isAuthorized=no