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Creep-rupture prediction by naive bayes classifiers

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος: Creep-rupture prediction by naive bayes classifiers
Συγγραφείς: Darwiche, Mohamad, Bousaleh, Ghazi, Feuilloy, Mathieu, Schang, Daniel, El Guerjouma, Rachid
Συνεισφορές: Laboratoire d'Acoustique de l'Université du Mans (LAUM), Le Mans Université (UM)-Centre National de la Recherche Scientifique (CNRS), الجامعة اللبنانية بيروت = Lebanese University Beirut = Université libanaise Beyrouth (LU / ULB), École supérieure d'électronique de l'ouest Angers (ESEO), Société Française d'Acoustique
Πηγή: Acoustics 2012 ; https://hal.science/hal-00811248 ; Acoustics 2012, Apr 2012, Nantes, France
Στοιχεία εκδότη: CCSD
Έτος έκδοσης: 2012
Συλλογή: Le Mans Université: Archives Ouvertes (HAL)
Θεματικοί όροι: artificial learning algorithm, creep experiment, acoustic, life-time prediction, [SPI.ACOU]Engineering Sciences [physics]/Acoustics [physics.class-ph]
Θέμα γεωγραφικό: Nantes, France
Περιγραφή: International audience ; The purpose of this study was to predict the failure of composite materials by developing and evaluating an artificial learning algorithm that could predict their life time. This will be done by predicting whether a specimen will break within 30 seconds or not. Specimens were tested according to the creep test by the traction method. Naive Bayesian classifiers have been developed retrospectively in a group of 90 samples and tested prospectively in a group of 30 samples to evaluate and ensure the performance of this learning method. Each sample was characterized by a number of relevant parameters. During the five cross-validations, the learning machine achieved a mean sensitivity of 78% and a mean specificity of 82%. The mean area under the ROC curve (Receiver Operating Curves) reached 0.88. The study can be regarded as a very important step in the term of prediction of composite material time life remaining.
Τύπος εγγράφου: conference object
Γλώσσα: English
Διαθεσιμότητα: https://hal.science/hal-00811248
https://hal.science/hal-00811248v1/document
https://hal.science/hal-00811248v1/file/hal-00811248.pdf
Rights: https://about.hal.science/hal-authorisation-v1/ ; info:eu-repo/semantics/OpenAccess
Αριθμός Καταχώρησης: edsbas.992CCDA7
Βάση Δεδομένων: BASE
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  Data: <searchLink fieldCode="AR" term="%22Darwiche%2C+Mohamad%22">Darwiche, Mohamad</searchLink><br /><searchLink fieldCode="AR" term="%22Bousaleh%2C+Ghazi%22">Bousaleh, Ghazi</searchLink><br /><searchLink fieldCode="AR" term="%22Feuilloy%2C+Mathieu%22">Feuilloy, Mathieu</searchLink><br /><searchLink fieldCode="AR" term="%22Schang%2C+Daniel%22">Schang, Daniel</searchLink><br /><searchLink fieldCode="AR" term="%22El+Guerjouma%2C+Rachid%22">El Guerjouma, Rachid</searchLink>
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  Data: Laboratoire d'Acoustique de l'Université du Mans (LAUM)<br />Le Mans Université (UM)-Centre National de la Recherche Scientifique (CNRS)<br />الجامعة اللبنانية بيروت = Lebanese University Beirut = Université libanaise Beyrouth (LU / ULB)<br />École supérieure d'électronique de l'ouest Angers (ESEO)<br />Société Française d'Acoustique
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  Data: Acoustics 2012 ; https://hal.science/hal-00811248 ; Acoustics 2012, Apr 2012, Nantes, France
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  Data: International audience ; The purpose of this study was to predict the failure of composite materials by developing and evaluating an artificial learning algorithm that could predict their life time. This will be done by predicting whether a specimen will break within 30 seconds or not. Specimens were tested according to the creep test by the traction method. Naive Bayesian classifiers have been developed retrospectively in a group of 90 samples and tested prospectively in a group of 30 samples to evaluate and ensure the performance of this learning method. Each sample was characterized by a number of relevant parameters. During the five cross-validations, the learning machine achieved a mean sensitivity of 78% and a mean specificity of 82%. The mean area under the ROC curve (Receiver Operating Curves) reached 0.88. The study can be regarded as a very important step in the term of prediction of composite material time life remaining.
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