@inproceedings{bb2bb37ec653471fbf1bef69218f5755,

title = "Reliability assessment using probabilistic support vector machines (PSVMs)",

abstract = "This article presents a new probability of failure measure based on the notion of probabilistic support vector machines (PSVMs). A PSVM allows one to quantify the probability of having an error in the approximation of the failure boundary using a support vector machine (SVM). SVM can define explicitly the boundaries of disjoint and non-convex failure domains. The approximation of the failure boundary can be refined using an adaptive sampling scheme with a limited number of samples. However, the calculation of the probability of failure might still be inaccurate despite the adaptive sampling. In order to refine the probability estimate, the {"}quality{"} of the approximated boundary is quantified through the probability of misclassification of a sample by the SVM. A new measure of probability is then calculated using Monte-Carlo simulations that include the probability of misclassification. The proposed measure of probability of failure is such that it is always larger (i.e., more conservative) than the one obtained using a deterministic SVM. Several analytical examples are presented, including a case with two failure modes.",

author = "Anirban Basudhar and Samy Missoum",

year = "2010",

doi = "10.2514/6.2010-2764",

language = "English (US)",

isbn = "9781600867422",

series = "Collection of Technical Papers - AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference",

publisher = "American Institute of Aeronautics and Astronautics Inc.",

booktitle = "51st AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference",

}