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Yun Wanying, Lü Zhenzhou, Jiang Xian. AN EFFICIENT METHOD FOR FAILURE PROBABILITY-BASED MOMENT-INDEPENDENT SENSITIVITY ANALYSIS[J]. Chinese Journal of Theoretical and Applied Mechanics, 2016, 48(4): 1004-1012. DOI: 10.6052/0459-1879-15-411
Citation: Yun Wanying, Lü Zhenzhou, Jiang Xian. AN EFFICIENT METHOD FOR FAILURE PROBABILITY-BASED MOMENT-INDEPENDENT SENSITIVITY ANALYSIS[J]. Chinese Journal of Theoretical and Applied Mechanics, 2016, 48(4): 1004-1012. DOI: 10.6052/0459-1879-15-411

AN EFFICIENT METHOD FOR FAILURE PROBABILITY-BASED MOMENT-INDEPENDENT SENSITIVITY ANALYSIS

  • Received Date: November 10, 2015
  • Revised Date: April 11, 2016
  • The failure probability-based moment-independent sensitivity index well analyzes how uncertainty in the failure probability of a model can be apportioned to different sources of uncertainty in the model inputs. At present, the existing sampling-based methods to estimate this index can not make full use of samples. Therefore, in this paper, we mainly concern how to improve the utilization of samples to accurately estimate this index. Based on the law of total variance in the successive intervals without overlapping proved in this paper, we propose an efficient method to estimate the failure probability-based moment-independent sensitivity index by combining the idea of space-partition and importance sampling, which only requires one set of input-output samples and the computational cost is independent of the dimensionality of inputs. The proposed method firstly uses importance sampling density function which can promise that a large number of samples will drop into the failure domain to generate a set of samples and then simultaneously obtain the sensitivity indices for all the input variables by repeatedly using this single set of samples. It is because of this that proposed method greatly improves the utilization of samples. Examples in this paper illustrate that our proposed method has higher efficiency, accuracy, convergence and robustness than the existing ones, and demonstrate its good prospect in engineering applications.
  • 1 Saltelli A. Sensitivity analysis for importance assessment. Risk Analysis, 2002, 22(3): 579-590
    2 Borgonovo E, Apostolakis GE. A new importance measure for risk-informed decision-making. Reliability Engineering and System Safety, 2001, 72(2): 193-212
    3 Borgonovo E, Apostolakis GE, Tarantola S, et al. Comparison of local and global sensitivity analysis techniques in probability safety assessment. Reliability Engineering and System Safety, 2003, 79: 175-185
    4 Saltelli A, Ratto M, Andreffs T, et al. Global Sensitivity Analysis. The Primer. John Wiley & Sons, 2008
    5 Saltelli A, Marivoet J. Non-parametric statistics in sensitivity analysis for model output: a comparison of selected techniques. Reliability Engineering and System Safety, 1990, 28(2): 229-253
    6 Iman RL, Johnson ME, Watson Jr CC. Sensitivity analysis for computer model projections of hurricane losses. Risk analysis, 2005, 25(5): 1277-1297
    7 Zhang XF, Pandey MD. An effective approximation for variancebased global sensitivity analysis. Reliability Engineering and System Safety, 2014, 121: 164-174
    8 郝文锐,吕震宙,魏鹏飞. 多项式输出中相关变量的重要性测度分析. 力学学报,2012,44(1): 167-173 (HaoWenrui, Lü Zhenzhou, Wei Pengfei. Importance measure of correlated variables in polynomial output. Chinese Journal of Theoretical and Applied Mechanics, 2012, 44(1): 167-173 (in Chinese))
    9 Wei P, Lu ZZ, Song JW. A new variance-based global sensitivity analysis technique. Computation Physics Communication, 2013, 184(10): 2540-2551
    10 Deman G, Konakli K, Sudret B, et al. Using sparse polynomial chaos expansions for the global sensitivity analysis of groundwater lifetime expectancy in mult-layered hydrogeological model. Reliability Engineering and System Safety, 2016, 147: 156-169
    11 Pianosi F, Wagener T. A simple and efficient method for global sensitivity analysis based on cumulative distribution functions. Environmental Modelling & Software, 2015, 67: 1-11
    12 Borgonovo E. A new uncertainty importance measure. Reliability Engineering and System Safety, 2007, 92(6): 771-784
    13 Zhou CC, Lu ZZ, Zhang LG, et al. Moment independent sensitivity analysis with correlations. Applied Mathematical Modelling, 2014, 38(19): 4885-4896
    14 Camboa F, Klein T, Lagnoux A. Sensitivity analysis based on Cramer von Mises Distance. arXiv: 1506.04133 [math.PR], 1-20
    15 Cui LJ, Lu ZZ, Zhao XP. Moment-independent importance measure of basic random variable and its probability density evolution solution. Science China Technological Sciences, 2010, 53(4): 1138-1145
    16 Li LY, Lu ZZ, Feng J, et al. Moment-independent importance measure of basic variable and its state dependent parameter solution. Structural Safety, 2012, 38: 40-47
    17 张磊刚, 吕震宙,陈军. 基于失效概率的矩独立重要性测度的高效算法. 航空学报,2014,35(8): 2199-2206 (Zhang Leigang, Lü Zhenzhou, Chen Jun. An efficient method of failure probabilitybased moment-independent importance measure. Acta Aeronautica et Astronautica Sinica, 2014, 35(8): 2199-2206 (in Chinese))
    18 Wei PF, Lu ZZ, Hao WR, et al. Efficient sampling methods for global reliability sensitivity analysis. Computation Physics Communication, 2012, 183(8): 1728-1743
    19 Plischke E, Borgonovo E, Smith CL. Global sensitivity measures from given data. European Journal of Operational Research, 2013, 226(3): 536-550
    20 Zhai QQ, Yang J, Zhao Y. Space-partition method for the variancebased sensitivity analysis: Optimal partition scheme and comparative study. Reliability Engineering and System Safety, 2014, 131: 66-82
    21 Mood AM, Graybill FA, Boes DC. Introduction to the Theory of Statistics. 3rd edn. McGraw-Hill, 1974
    22 Harbitz A. An efficient sampling method for probability of failure calculation. Structural Safety, 1986, 3(2): 109-115
    23 Melchers RE. Importance sampling in structural system. Structural Safety, 1989, 6(1): 3-10
    24 Au SK, Beck JL. A new adaptive importance sampling scheme. Structural Safety, 1999, 21(2): 135-158
    25 Au SK, Beck JL. Importance sampling in high dimensions. Structural Safety, 2003, 25(2): 139-163
    26 戴鸿哲,薛国锋,王伟. 基于小波阈值密度的自适应重要抽样方法. 力学学报,2014, 46(3): 480-484 (Dai Hongzhe, Xue Guofeng, Wang Wei. A wavelet thresholding density-based adaptive importance sampling method. Chinese Journal of Theoretical and Applied Mechanics, 2014, 46(3): 480-484 (in Chinese))
    27 Hasfer AM, Lind NC. Exact and invariant second moment code format. Journal of Engineering Mechanics, ASCE, 1974, 100(1): 111-121
    28 Dai HZ,WangW. Application of low-discrepancy sampling method in structural reliability analysis. Structural Safety, 2009, 31(1): 55-64
    29 Sobol IM. Uniformly distributed sequences with additional uniformity properties. USSR Computational Mathematics and Mathematical Physics, 1976, 16: 236-242
    30 Sobol IM. On quasi-Monte Carlo integrations. Mathematics and Computers in Simulation, 1998, 47(2): 103-112
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