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  • Open Access


    Comparison of Structural Probabilistic and Non-Probabilistic Reliability Computational Methods under Big Data Condition

    Yongfeng Fang1,3, Kong Fah Tee2,*

    Structural Durability & Health Monitoring, Vol.16, No.2, pp. 129-143, 2022, DOI:10.32604/sdhm.2022.020301

    Abstract In this article, structural probabilistic and non-probabilistic reliability have been evaluated and compared under big data condition. Firstly, the big data is collected via structural monitoring and analysis. Big data is classified into different types according to the regularities of the distribution of data. The different stresses which have been subjected by the structure are used in this paper. Secondly, the structural interval reliability and probabilistic prediction models are established by using the stress-strength interference theory under big data of random loads after the stresses and structural strength are comprehensively considered. Structural reliability is computed More >

  • Open Access


    A Bayesian Updating Method for Non-Probabilistic Reliability Assessment of Structures with Performance Test Data

    Jiaqi He1, Yangjun Luo1,2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.2, pp. 777-800, 2020, DOI:10.32604/cmes.2020.010688

    Abstract For structures that only the predicted bounds of uncertainties are available, this study proposes a Bayesian method to logically evaluate the nonprobabilistic reliability of structures based on multi-ellipsoid convex model and performance test data. According to the given interval ranges of uncertainties, we determine the initial characteristic parameters of a multi-ellipsoid convex set. Moreover, to update the plausibility of characteristic parameters, a Bayesian network for the information fusion of prior uncertainty knowledge and subsequent performance test data is constructed. Then, an updated multi-ellipsoid set with the maximum likelihood of the performance test data can be More >

  • Open Access


    A Comprehensive Model for Structural Non-Probabilistic Reliability and the Key Algorithms

    Wencai Sun1, ∗, Zichun Yang1

    CMES-Computer Modeling in Engineering & Sciences, Vol.123, No.1, pp. 309-332, 2020, DOI:10.32604/cmes.2020.08386

    Abstract It is very difficult to know the exact boundaries of the variable domain for problems with small sample size, and the traditional convex set model is no longer applicable. In view of this, a novel reliability model was proposed on the basis of the fuzzy convex set (FCS) model. This new reliability model can account for different relations between the structural failure region and variable domain. Key computational algorithms were studied in detail. First, the optimization strategy for robust reliability is improved. Second, Monte Carlo algorithms (i.e., uniform sampling method) for hyper-ellipsoidal convex sets were More >

  • Open Access


    A Non-probabilistic Reliability-based Optimization of Structures Using Convex Models

    Fangyi Li1,2, Zhen Luo3, Jianhua Rong1, Lin Hu1

    CMES-Computer Modeling in Engineering & Sciences, Vol.95, No.6, pp. 453-482, 2013, DOI:10.3970/cmes.2013.095.453

    Abstract This paper aims to propose a non-probabilistic reliability-based multiobjective optimization method for structures with uncertain-but-bounded parameters. A combination of the interval and ellipsoid convex models is used to account for the different groups of uncertain parameters, in which the interval model accounts for uncorrelated parameters, while the ellipsoid model is applied to correlated parameters. The design is then formulated as a nested double-loop optimization problem. A multi-objective genetic algorithm is used in the out loop optimization to optimize the design vector for evaluating the objectives, and the Sequential Quadratic Programming (SQP) algorithm is applied in… More >

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