An innovative adaptive sparse response surface method for structural reliability analysis

ISISE Institute for Sustainability and Innovation in Structural Engineering (PEst-C/ECI/UI4029/2011 FCOM-01-0124-FEDER-022681), FCT Portuguese Scientific Foundation for the research grant PD/BD/113677/2015 under the PhD Programme “Analysis and Mitigation of Risks in Infrastructures InfraRisk-”. The third author would also like to thank the project POCI-01-0145-FEDER-007457 – CONSTRUCT – Institute of R&D in Structures and Construction. This work was also partly financed by FEDER funds through the Competitivity Factors Operational Programme – COMPETE and by national funds through FCT Foundation for Science and Technology within the scope of the project POCI-01-0145-FEDER-007633

[1]  Hongping Zhu,et al.  Assessing small failure probabilities by AK–SS: An active learning method combining Kriging and Subset Simulation , 2016 .

[2]  C. Bucher,et al.  A fast and efficient response surface approach for structural reliability problems , 1990 .

[3]  Nicolas Gayton,et al.  AK-MCS: An active learning reliability method combining Kriging and Monte Carlo Simulation , 2011 .

[4]  J. Beck,et al.  Important sampling in high dimensions , 2003 .

[5]  M. Sergent,et al.  Constructing space-filling designs using an adaptive WSP algorithm for spaces with constraints , 2014 .

[6]  M. Lemaire,et al.  Stochastic finite element: a non intrusive approach by regression , 2006 .

[7]  Thomas J. Santner,et al.  The Design and Analysis of Computer Experiments , 2003, Springer Series in Statistics.

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

[9]  G. G. Wang,et al.  Efficient adaptive response surface method using intelligent space exploration strategy , 2015 .

[10]  Blaise Hanczar,et al.  The reliability of estimated confidence intervals for classification error rates when only a single sample is available , 2013, Pattern Recognit..

[11]  Andrzej S. Nowak,et al.  Reliability of Structures , 2000 .

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

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

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

[15]  Jorge E. Hurtado,et al.  Neural-network-based reliability analysis: a comparative study , 2001 .

[16]  B. Sudret,et al.  An adaptive algorithm to build up sparse polynomial chaos expansions for stochastic finite element analysis , 2010 .

[17]  H. Gomes,et al.  COMPARISON OF RESPONSE SURFACE AND NEURAL NETWORK WITH OTHER METHODS FOR STRUCTURAL RELIABILITY ANALYSIS , 2004 .

[18]  A. Sellier,et al.  Adaptive response surface method based on a double weighted regression technique , 2009 .

[19]  L. Schueremans,et al.  Benefit of splines and neural networks in simulation based structural reliability analysis , 2005 .

[20]  Jerome Sacks,et al.  Choosing the Sample Size of a Computer Experiment: A Practical Guide , 2009, Technometrics.

[21]  Bertrand Iooss,et al.  Numerical studies of the metamodel fitting and validation processes , 2010, 1001.1049.

[22]  Michel Salaün,et al.  A new adaptive response surface method for reliability analysis , 2013 .

[23]  Siu-Kui Au,et al.  Application of subset simulation methods to reliability benchmark problems , 2007 .

[24]  Jon C. Helton,et al.  Extension of Latin hypercube samples with correlated variables , 2008, Reliab. Eng. Syst. Saf..

[25]  Jinsuo Nie,et al.  A new directional simulation method for system reliability. Part II: application of neural networks , 2004 .

[26]  A. Kiureghian,et al.  Multivariate distribution models with prescribed marginals and covariances , 1986 .

[27]  Robert E. Melchers,et al.  Effect of response surface parameter variation on structural reliability estimates , 2001 .

[28]  Jorge E. Hurtado,et al.  An examination of methods for approximating implicit limit state functions from the viewpoint of statistical learning theory , 2004 .

[29]  Sankaran Mahadevan,et al.  Efficient surrogate models for reliability analysis of systems with multiple failure modes , 2011, Reliab. Eng. Syst. Saf..

[30]  Irfan Kaymaz,et al.  Application Of Kriging Method To Structural Reliability Problems , 2005 .

[31]  Enrico Zio,et al.  An improved adaptive kriging-based importance technique for sampling multiple failure regions of low probability , 2014, Reliab. Eng. Syst. Saf..

[32]  Vincenzo Ilario Carbone,et al.  An improvement of the response surface method , 2011 .

[33]  N. Lazar,et al.  Methods and Criteria for Model Selection , 2004 .

[34]  S. Chakraborty,et al.  Reliability analysis of structures by iterative improved response surface method , 2016 .

[35]  Timothy W. Simpson,et al.  Metamodels for Computer-based Engineering Design: Survey and recommendations , 2001, Engineering with Computers.

[36]  P. Bjerager.  Probability Integration by Directional Simulation , 1988 .

[37]  B. Sudret,et al.  Metamodel-based importance sampling for structural reliability analysis , 2011, 1105.0562.

[38]  Qiusheng Li,et al.  A new artificial neural network-based response surface method for structural reliability analysis , 2008 .

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

[40]  Nicolas Gayton,et al.  RPCM: a strategy to perform reliability analysis using polynomial chaos and resampling , 2010 .

[41]  P. Das,et al.  Cumulative formation of response surface and its use in reliability analysis , 2000 .

[42]  Maurice Lemaire,et al.  Assessing small failure probabilities by combined subset simulation and Support Vector Machines , 2011 .

[43]  Sang Hyo Kim,et al.  Response surface method using vector projected sampling points , 1997 .

[44]  J.-M. Bourinet,et al.  Rare-event probability estimation with adaptive support vector regression surrogates , 2016, Reliab. Eng. Syst. Saf..

[45]  Soo-Chang Kang,et al.  An efficient response surface method using moving least squares approximation for structural reliability analysis , 2010 .

[46]  J. C. Helton,et al.  An Investigation of Uncertainty and Sensitivity Analysis Techniques for Computer Models , 1988 .

[47]  Jorge E. Hurtado,et al.  Classification Approach for Reliability Analysis with Stochastic Finite-Element Modeling , 2003 .

[48]  Enrico Zio,et al.  Computing confidence and prediction intervals of industrial equipment degradation by bootstrapped support vector regression , 2015, Reliab. Eng. Syst. Saf..

[49]  B. Sudret,et al.  Reliability-based design optimization using kriging surrogates and subset simulation , 2011, 1104.3667.

[50]  W. K. Hastings,et al.  Monte Carlo Sampling Methods Using Markov Chains and Their Applications , 1970 .

[51]  Marco Cavazzuti,et al.  Optimization Methods: From Theory to Design Scientific and Technological Aspects in Mechanics , 2012 .

[52]  I. Kaymaz,et al.  A response surface method based on weighted regression for structural reliability analysis , 2005 .

[53]  Jianyun Chen,et al.  Improved response surface method for anti-slide reliability analysis of gravity dam based on weighted regression , 2010 .

[54]  Francesco Cadini,et al.  A Bayesian Monte Carlo-based algorithm for the estimation of small failure probabilities of systems affected by uncertainties , 2016, Reliab. Eng. Syst. Saf..

[55]  N. Gayton,et al.  CQ2RS: a new statistical approach to the response surface method for reliability analysis , 2003 .

[56]  George Michailidis,et al.  Sequential Experiment Design for Contour Estimation From Complex Computer Codes , 2008, Technometrics.

[57]  João M. Rocha,et al.  Safety assessment of a short span railway bridge for high-speed traffic using simulation techniques , 2012 .

[58]  Bertrand Iooss,et al.  Numerical studies of space-filling designs: optimization of Latin Hypercube Samples and subprojection properties , 2013, J. Simulation.

[59]  G. G. Wang,et al.  Adaptive Response Surface Method Using Inherited Latin Hypercube Design Points , 2003 .

[60]  Sharif Rahman,et al.  Structural reliability analysis by univariate decomposition and numerical integration , 2007 .

[61]  Nicolas Gayton,et al.  A combined Importance Sampling and Kriging reliability method for small failure probabilities with time-demanding numerical models , 2013, Reliab. Eng. Syst. Saf..