Moment-based evaluation of structural reliability

Abstract Reliability analysis is a widely-used tool to measure a structure’s ability of fulfilling safety and serviceability requirements. Existing methods for reliability assessment have, for the most part, been developed based on using the probability distribution functions of input random variables accounting for the uncertainties arising from both structural properties (e.g., material strength, geometry) and external loads. This paper develops a moment-based method for reliability assessment, which relies on the moment information of input variables rather than the probability distribution functions. It is shown that the proposed method is useful in solving multi-dimensional reliability assessment problems with improved efficiency compared with Monte Carlo simulation. The implementation, validity and efficiency of the proposed method are demonstrated through illustrative examples.

[1]  W. L. Dunn,et al.  Exploring Monte Carlo Methods , 2011 .

[2]  Hongzhe Dai,et al.  A multiwavelet support vector regression method for efficient reliability assessment , 2015, Reliab. Eng. Syst. Saf..

[3]  Quanwang Li,et al.  Reliability assessment of aging structures subjected to gradual and shock deteriorations , 2017, Reliab. Eng. Syst. Saf..

[4]  Yan-Gang Zhao,et al.  Fourth-Moment Standardization for Structural Reliability Assessment , 2007 .

[5]  A. Kiureghian,et al.  Second-Order Reliability Approximations , 1987 .

[6]  Sheldon M. Ross.  Introduction to Probability Models. , 1995 .

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

[8]  Bruce R. Ellingwood,et al.  A risk de-aggregation framework that relates community resilience goals to building performance objectivess , 2016 .

[9]  N. Null.  Minimum Design Loads for Buildings and Other Structures , 2003 .

[10]  Dan M. Frangopol,et al.  Reliability-based performance indicators for structural members , 2016 .

[11]  C. C. Heyde,et al.  On a Property of the Lognormal Distribution , 1963 .

[12]  Cao Wang,et al.  Roles of load temporal correlation and deterioration-load dependency in structural time-dependent reliability , 2018 .

[13]  Ronald L. Wasserstein,et al.  Monte Carlo: Concepts, Algorithms, and Applications , 1997 .

[14]  Bruce R. Ellingwood,et al.  LRFD: implementing structural reliability in professional practice , 2000 .

[15]  Quanwang Li,et al.  Estimating the time-dependent reliability of aging structures in the presence of incomplete deterioration information , 2016 .

[16]  R. Melchers.  Importance sampling in structural systems , 1989 .

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

[18]  Bruce R. Ellingwood,et al.  Confidence intervals for reliability indices using likelihood ratio statistics , 2012 .

[19]  Quanwang Li,et al.  Time-dependent reliability of ageing structures: an approximate approach , 2016 .

[20]  Palle Thoft-Christensen,et al.  Structural Reliability Theory and Its Applications , 1982 .

[21]  Quanwang Li,et al.  Time-dependent reliability of aging structures in the presence of non-stationary loads and degradation , 2015 .

[22]  Wilson H. Tang,et al.  Probability Concepts in Engineering: Emphasis on Applications to Civil and Environmental Engineering , 2006 .

[23]  M. Shinozuka.  Basic Analysis of Structural Safety , 1983 .

[24]  Bruce R. Ellingwood,et al.  Probability-based codified design: past accomplishments and future challenges , 1994 .

[25]  Yan-Gang Zhao,et al.  SYSTEM RELIABILITY ASSESSMENT BY METHOD OF MOMENTS , 2003 .

[26]  Tsuyoshi Takada,et al.  Macrospatial Correlation Model of Seismic Ground Motions , 2005 .

[27]  R. Nelsen.  An Introduction to Copulas , 1998 .

[28]  Dirk P. Kroese,et al.  Why the Monte Carlo method is so important today , 2014 .