{"id":{"repo_id":"nus","oai_identifier":"oai:scholarbank.nus.edu.sg:10635/151245"},"canonical_url":"https://search.dev.ndltd.org/etd/nus/oai:scholarbank.nus.edu.sg:10635/151245","repository":{"repo_id":"nus","name":"National University of Singapore","base_url":"https://scholarbank.nus.edu.sg/oai/request"},"display":{"title":"STRUCTURAL RELIABILITY ANALYSIS BASED ON MAXIMUM ENTROPY DISTRIBUTION WITH FRACTIONAL MOMENTS","abstract":"Structural reliability analysis involves low-probability-failure prediction to account for the rare events occurrence rate. To predict the low probability of failure of offshore drilling riser system accurately and efficiently is crucial but challenging. An improved univariate density estimation method based on maximum entropy theory is proposed in this thesis to perform structural reliability analysis. The bias and variance error are smaller than the methods compared, and the model is flexible and applicable to many applications. Multivariate analysis is particularly useful for modelling the joint distribution of structural input parameters; with potential to predict failure probability of structural systems with multiple failure modes. An improved multivariate maximum entropy distribution with fractional moment constraints is proposed in this thesis. The accuracy and flexibility of the improved multivariate density estimation method is illustrated with several theoretical distributions and engineering applications.","abstract_html":"Structural reliability analysis involves low-probability-failure prediction to account for the rare events occurrence rate. To predict the low probability of failure of offshore drilling riser system accurately and efficiently is crucial but challenging. An improved univariate density estimation method based on maximum entropy theory is proposed in this thesis to perform structural reliability analysis. The bias and variance error are smaller than the methods compared, and the model is flexible and applicable to many applications. Multivariate analysis is particularly useful for modelling the joint distribution of structural input parameters; with potential to predict failure probability of structural systems with multiple failure modes. An improved multivariate maximum entropy distribution with fractional moment constraints is proposed in this thesis. 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The bias and variance error are smaller than the methods compared, and the model is flexible and applicable to many applications. Multivariate analysis is particularly useful for modelling the joint distribution of structural input parameters; with potential to predict failure probability of structural systems with multiple failure modes. An improved multivariate maximum entropy distribution with fractional moment constraints is proposed in this thesis. 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