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Combined size and shape optimization of truss structures using subset simulation optimization
7 references·2 citing·29 second hop
Combined size and shape optimization of truss structures using subset simulation optimization (2018)Combined size and shape optimization of t…Rare event simulation in finite-infinite dimensional space (2015)Rare event simulation in fini…On MCMC algorithm for Subset Simulation (2015)On MCMC algorithm for Subset …MCMC algorithms for Subset Simulation (2015)MCMC algorithms for Subset Si…Discrete Optimum Design for Truss Structures by Subset Simulation Algorithm (2014)Discrete Optimum Design for T…Structural Optimization of a Nose Landing Gear Considering Its Fatigue Life (2012)Structural Optimization of a …Application of subset simulation methods to reliability benchmark problems (2006)Application of subset simulat…Estimation of small failure probabilities in high dimensions by subset simulation (2001)Estimation of small failure p…Accelerating subset simulation with surrogate model ensuring unbiased failure probability estimation (2025)Accelerating subset simulatio…Failure probability analysis of heliostat systems (2020)Failure probability analysis …Reliability-based design sensitivity by efficient simulation (2005)Subset simulation method including fitness-based seed selection for reliability analysis (2020)On the Experimental Attainment of Optimum Conditions (1951)Design and Analysis of Experiments (1992)On the Experimental Attainment of Optimum Conditions (1992)Simulation and the Monte Carlo Method (1981)The stochastic finite element method: Past, present and future (2008)A combined Importance Sampling and Kriging reliability method for small failure probabilities with time-demanding numerical models (2012)The generalization of Latin hypercube sampling (2015)Simulation and the Monte Carlo Method (2016)Reliability analysis—a review and some perspectives (2001)A critical appraisal of reliability estimation procedures for high dimensions (2004)Benchmark study on reliability estimation in higher dimensions of structural systems – An overview (2006)Reliability engineering (2011)Monte Carlo and variance reduction methods for structural reliability analysis: A comprehensive review (2023)AK-MCS: An active learning reliability method combining Kriging and Monte Carlo Simulation (2011)Robust artificial neural network for reliability and sensitivity analyses of complex non-linear systems (2017)Sensitivity estimation of failure probability applying line sampling (2017)A hybrid Kriging-based reliability method for small failure probabilities (2019)Reliability-based Structural Design (2006)Engineering Risk Assessment with Subset Simulation (2014)A fast and efficient response surface approach for structural reliability problems (1990)Stochastic Finite Elements: A Spectral Approach (1991)Simulation and the Monte Carlo Method (2007)Bayesian Updating with Structural Reliability Methods (2014)Sequential importance sampling for structural reliability analysis (2016)The stepwise accuracy-improvement strategy based on the Kriging model for structural reliability analysis (2018)An efficient and robust adaptive Kriging for structural reliability analysis (2020)Importance Sampling for Time-Variant Reliability Analysis (2021)
this work references cited by second hop

References

  • 2001Estimation of small failure probabilities in high dimensions by subset simulation2,419 cited
  • 2015MCMC algorithms for Subset Simulation413 cited
  • 2006Application of subset simulation methods to reliability benchmark problems285 cited
  • 2015Rare event simulation in finite-infinite dimensional space75 cited
  • 2015On MCMC algorithm for Subset Simulation40 cited
  • 2014Discrete Optimum Design for Truss Structures by Subset Simulation Algorithm34 cited
  • 2012Structural Optimization of a Nose Landing Gear Considering Its Fatigue Life25 cited

Cited by

  • 2020Failure probability analysis of heliostat systems10 cited
  • 2025Accelerating subset simulation with surrogate model ensuring unbiased failure probability estimation8 cited

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