Application of low-discrepancy sampling method in structural reliability analysis

This study introduces and investigates various low-discrepancy sequences and then develops a new procedure in which the low-discrepancy sequences are combined with the importance sampling technique to estimate the failure probability. This proposed low-discrepancy sampling method is based on the concept that the deterministic low-discrepancy sequences of points can significantly improve the accuracy of the classical Monte Carlo (MC) method over purely random sampling. Different benchmark examples verify that the proposed method is more accurate with the same number of samples and has a faster rate of convergence in order to achieve a given accuracy when compared with the MC method. Therefore, the low-discrepancy sampling method shows great potential for improving the accuracy and efficiency of the MC-based simulation method for structural reliability analysis.

[1]  A. Kiureghian,et al.  Optimization algorithms for structural reliability , 1991 .

[2]  H. Keng,et al.  Applications of number theory to numerical analysis , 1981 .

[3]  A. M. Hasofer,et al.  Exact and Invariant Second-Moment Code Format , 1974 .

[4]  R. Rackwitz,et al.  A benchmark study on importance sampling techniques in structural reliability , 1993 .

[5]  Rüdiger Rackwitz,et al.  UPDATING FIRST-AND SECOND-ORDER RELIABILITY ESTIMATES BY IMPORTANCE SAMPLING , 1988 .

[6]  K. Fang,et al.  Application of Threshold-Accepting to the Evaluation of the Discrepancy of a Set of Points , 1997 .

[7]  Russel E. Caflisch,et al.  Quasi-Random Sequences and Their Discrepancies , 1994, SIAM J. Sci. Comput..

[8]  A. Olsson,et al.  On Latin hypercube sampling for structural reliability analysis , 2003 .

[9]  Kalman Ziha,et al.  Descriptive sampling in structural safety , 1995 .

[10]  Robert E. Melchers,et al.  General multi-dimensional probability integration by directional simulation , 1990 .

[11]  Fred J. Hickernell,et al.  Randomized Halton sequences , 2000 .

[12]  K. Fang,et al.  Number-theoretic methods in statistics , 1993 .

[13]  Harald Niederreiter,et al.  Random number generation and Quasi-Monte Carlo methods , 1992, CBMS-NSF regional conference series in applied mathematics.

[14]  J. Halton On the efficiency of certain quasi-random sequences of points in evaluating multi-dimensional integrals , 1960 .

[15]  B. Ellingwood,et al.  Directional methods for structural reliability analysis , 2000 .

[16]  Dimos C. Charmpis,et al.  Application of line sampling simulation method to reliability benchmark problems , 2007 .

[17]  D. Huntington,et al.  Improvements to and limitations of Latin hypercube sampling , 1998 .

[18]  G. Ökten,et al.  Randomized quasi-Monte Carlo methods in pricing securities , 2004 .

[19]  R. Melchers Search-based importance sampling , 1990 .

[20]  J. Beck,et al.  Estimation of Small Failure Probabilities in High Dimensions by Subset Simulation , 2001 .

[21]  R. Rackwitz,et al.  Structural reliability under combined random load sequences , 1978 .

[22]  Robert E. Melchers,et al.  Structural Reliability: Analysis and Prediction , 1987 .

[23]  G. Schuëller,et al.  A critical appraisal of methods to determine failure probabilities , 1987 .

[24]  Jinsuo Nie,et al.  A new directional simulation method for system reliability. Part I: application of deterministic point sets , 2004 .

[25]  Art B. Owen,et al.  Monte Carlo extension of quasi-Monte Carlo , 1998, 1998 Winter Simulation Conference. Proceedings (Cat. No.98CH36274).

[26]  Yaacob Ibrahim,et al.  Observations on applications of importance sampling in structural reliability analysis , 1991 .

[27]  Ronald Cools,et al.  Quasi-random integration in high dimensions , 2007, Math. Comput. Simul..

[28]  J. Yorke,et al.  Finding zeroes of maps: homotopy methods that are constructive with probability one , 1978 .

[29]  D. Frangopol,et al.  Hyperspace division method for structural reliability , 1994 .

[30]  Frances Y. Kuo,et al.  Quasi-Monte Carlo methods can be efficient for integration over products of spheres , 2005, J. Complex..