Article
Computer Science, Interdisciplinary Applications
Michael D. Shields, Dimitris G. Giovanis, V. S. Sundar
Summary: The paper proposes the use of an affine invariant ensemble MCMC sampler for conditional sampling to address extreme cases where subset simulation breaks down. The algorithm automatically varies step size and is particularly effective for estimating failure probabilities in strongly non-Gaussian and lower effective dimension scenarios.
COMPUTERS & STRUCTURES
(2021)
Article
Engineering, Civil
John Thedy, Kuo-Wei Liao
Summary: A novel Importance Sampling method for calculating reliability in structural engineering problems using multiple spheres was proposed, which maximizes the number of safety samples by introducing spheres with different radii and centers, leading to reduced computational cost and improved efficiency. Compared to traditional methods, the proposed approach demonstrates higher robustness and efficiency.
Article
Engineering, Multidisciplinary
Bin Xie, Chong Peng, Yanzhong Wang
Summary: This paper proposes a new reliability analysis method by combining relevance vector machine and subset simulation importance sampling, which improves the efficiency and accuracy of evaluating the failure probability of engineering structures involving implicit performance functions.
APPLIED MATHEMATICAL MODELLING
(2023)
Article
Engineering, Civil
Morgane Menz, Sylvain Dubreuil, Jerome Morio, Christian Gogu, Nathalie Bartoli, Marie Chiron
Summary: This paper presents a methodology to reduce the computational cost of reliability analysis for engineering problems involving complex numerical models using Gaussian process-based active learning methods. The sensitivity of the failure probability estimator to uncertainties generated by the Gaussian process and sampling strategy is quantified to control the overall error associated with the estimation. The proposed active learning approach aims to improve the estimation of rare event probabilities by reducing the main source of error and stopping when the global variability is sufficiently low, with its performance assessed on several examples.
Article
Computer Science, Interdisciplinary Applications
Yinghao Zhao, Zeyu Wang
Summary: Subset simulation (SS) is a computationally efficient method for estimating small failure probabilities and reducing emulation demands. ULSS is a new method that combines SS, importance sampling (IS), and DBSCAN algorithm to overcome the limitations of SS.
STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
(2022)
Article
Computer Science, Artificial Intelligence
Ning Wei, Zhenzhou Lu, Yingshi Hu
Summary: By introducing a Beta-hypersphere in traditional Radial-Based Importance Sampling (RBIS) method, efficiency of reliability analysis can be improved by avoiding the evaluation of Limit State Function (LSF) in safety samples located in the Beta-hypersphere. However, evaluation of LSF in safety samples outside the Beta-hypersphere is still necessary. To further enhance the efficiency, an Eccentric RBIS (ERBIS) method with an eccentric hypersphere is proposed, which can envelop more safety samples and avoid excess evaluation of LSF. The ERBIS method is demonstrated to significantly reduce LSF evaluations compared to the RBIS method.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Engineering, Civil
Hongyuan Guo, You Dong, Paolo Gardoni
Summary: This paper presents a novel method for time-dependent reliability analysis based on point evolution kernel density and adaptive subset simulation. The proposed method captures the cumulative density function of the first failure time to achieve time-dependent reliability analysis with high computational efficiency and accuracy.
Article
Engineering, Electrical & Electronic
Daniela B. Almeida, Carmen L. T. Borges, Gerson C. Oliveira, Mario Pereira
Summary: This paper introduces a new methodology for Monte Carlo-based multi-area reliability assessment using optimal Importance Sampling, Markov Chain Monte Carlo, and optimal stratification. The proposed methodology effectively represents features of variable renewable energy resources and has been demonstrated to achieve significant speedups in comparison with standard Monte-Carlo simulation through case studies based on the systems of Saudi Arabia and Chile.
ELECTRIC POWER SYSTEMS RESEARCH
(2021)
Article
Engineering, Mechanical
Hongyuan Guo, You Dong, Paolo Gardoni
Summary: The paper proposes a novel method for efficiently assessing the probability of rare failure events in structural engineering by integrating various techniques, and demonstrates the accuracy and efficiency of this method in different scenarios through numerical examples.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2022)
Article
Engineering, Industrial
Can Wang, Haipeng Xie, Zhaohong Bie, Gengfeng Li, Chao Yan
Summary: This study introduces a new method to rapidly evaluate the supply reliability of integrated power-gas systems, enhancing computational efficiency by optimizing the load shedding model and introducing importance sampling.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2021)
Article
Engineering, Mechanical
Axay Thapa, Atin Roy, Subrata Chakraborty
Summary: An adaptive Kriging based metamodeling approach for tunnel reliability analysis is proposed, which improves prediction accuracy by iteratively selecting new training points and updating the model until no points are left in the reduced space.
PROBABILISTIC ENGINEERING MECHANICS
(2022)
Article
Computer Science, Interdisciplinary Applications
Chunyan Ling, Zhenzhou Lu
Summary: The proposed method introduces a novel two-stage meta-model importance sampling based on support vector machine (SVM) to efficiently estimate structural failure probability. It provides an algorithm to efficiently deal with multiple failure regions and rare events, with the SVM model accurately recognizing the states of samples. Several examples are performed to show the feasibility of the proposed method.
STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
(2021)
Article
Engineering, Industrial
Atin Roy, Subrata Chakraborty
Summary: In this study, a three-stage adaptive support vector regression (SVR) model is built to alleviate the scarcity of samples in the reliability evaluation of structures with implicit limit state functions (LSFs). The model employs sequential and importance sampling techniques to ensure a sufficient number of simulation points near the failure plane for accurate estimation of reliability.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2022)
Article
Construction & Building Technology
Navid Rahgozar, Majid Pouraminian, Nima Rahgozar
Summary: This study focuses on the seismic reliability of Controlled-rocking steel cores (CRSCs) through extensive nonlinear dynamic analyses considering various random variables. The results show that the design procedure is reliable and the safety of CRSCs is provided, with the probability of failure for mid-rise CRSCs higher than low-rise archetypes.
JOURNAL OF BUILDING ENGINEERING
(2021)
Article
Computer Science, Information Systems
Jian Wang, Runan Cao, Zhili Sun
Summary: Importance sampling methods are widely used in time-independent reliability analysis, but have been rarely studied in time-variant reliability analysis. This article presents a method for time-variant reliability analysis that increases the probability of sampling failure trajectories. Validation results show that this method significantly improves sampling efficiency and accuracy compared to crude Monte Carlo simulation.
Letter
Engineering, Industrial
Pengfei Wei, Zhenzhou Lu, Xiukai Yuan
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2013)
Article
Engineering, Industrial
Xiukai Yuan, Zhenzhou Lu
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2014)
Article
Engineering, Multidisciplinary
Yuan Xiukai, Zheng Zhenxuan, Zhang Baoqiang
APPLIED MATHEMATICAL MODELLING
(2020)
Article
Computer Science, Interdisciplinary Applications
Matthias G. R. Faes, Marcos A. Valdebenito, Xiukai Yuan, Pengfei Wei, Michael Beer
Summary: This paper introduces an alternative approach to avoid the double loop in practical estimation of imprecise probability by replacing the problem with an augmented, purely aleatory reliability analysis. Through this approach, an explicit function of the imprecise parameters can be recovered from the augmented reliability problem, ultimately allowing the calculation of the imprecise probability. The proposed framework is shown to be highly efficient and accurate in two examples.
ADVANCES IN ENGINEERING SOFTWARE
(2021)
Article
Computer Science, Interdisciplinary Applications
Xiukai Yuan, Shaolong Liu, Marcos A. Valdebenito, Matthias G. R. Faes, Danko J. Jerez, Hector A. Jensen, Michael Beer
Summary: An efficient framework for reliability-based design optimization of structural systems is proposed, utilizing the failure probability function and posterior distribution for the optimization of design parameters.
ADVANCES IN ENGINEERING SOFTWARE
(2021)
Article
Engineering, Industrial
Xiukai Yuan, Matthias G. R. Faes, Shaolong Liu, Marcos A. Valdebenito, Michael Beer
Summary: This paper presents an efficient approach to compute the bounds on the reliability of a structure by decoupling a double-loop problem. The core idea is to infer a functional relationship between epistemic uncertain hyper-parameters and the probability of failure to determine the best and worst case behavior with respect to failure probability. Three case studies illustrate the effectiveness and efficiency of the developed methods.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2021)
Article
Computer Science, Interdisciplinary Applications
Xiukai Yuan, Jian Gu, Mingying Wu, Feng Zhang
Summary: This paper proposes a novel method for reliability-based design optimization by estimating the failure probability as a function of design variables through a weighted average of sample values generated by a single reliability analysis, decoupling the RBDO problem into a deterministic optimization problem, and iteratively seeking the solution using a sequential approximate optimization framework. Several examples demonstrate the high accuracy and efficiency of this method.
STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
(2021)
Article
Engineering, Civil
Xiukai Yuan, Shaolong Liu, M. A. Valdebenito, Jian Gu, Michael Beer
Summary: A novel procedure is proposed to estimate the failure probability function of design variables without distribution fitting, by transforming it into an expression that includes the posterior distribution and allowing it to be estimated by means of sampling. The proposed method shows efficient estimation of FPF within different simulation strategies and numerical examples demonstrate its performance.
Article
Engineering, Mechanical
Xiukai Yuan, Shaolong Liu, Matthias Faes, Marcos. A. Valdebenito, Michael Beer
Summary: This paper aims to assess the reliability of structures with deterioration processes and stochastic load processes using the importance sampling framework. The method transforms the time-dependent reliability problem into a series system with multiple performance functions based on the concept of composite limit states, and proposes an efficient two-step importance sampling density function. Practical examples demonstrate the effectiveness of the proposed approach.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2021)
Article
Engineering, Mechanical
Xiukai Yuan, Shanglong Wang, Marcos A. Valdebenito, Matthias G. R. Faes, Michael Beer
Summary: This paper proposes an efficient strategy to approximate the failure probability function in structural reliability problems. By introducing a new sample regeneration algorithm, efficiency is improved while ensuring high accuracy, and the effectiveness of the method is verified through case studies.
PROBABILISTIC ENGINEERING MECHANICS
(2023)
Article
Engineering, Industrial
Xiukai Yuan, Yugeng Qian, Jingqiang Chen, Matthias G. R. Faes, Marcos A. Valdebenito, Michael Beer
Summary: This paper presents an efficient approach for estimating the failure probability function (FPF) based on an adaptive strategy and a combination algorithm. The proposed approach involves a Weighted Importance Sampling approach, an adaptive strategy, and an optimal combination algorithm. Test and practical examples demonstrate the efficiency and feasibility of the approach.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2023)
Article
Engineering, Mechanical
Zhenhong Deng, Ming Zhan, Xiukai Yuan, Huageng Luo, Baoqiang Zhang
Summary: This study discusses two issues in implementing Bayesian model updating with the assumption of a Gaussian distribution. First, it presents an alternative approach to the problem of the normal likelihood function used in Markov chain Monte Carlo (MCMC) by defining the scaled likelihood ratio (SLR). Second, a data preprocessing technique is proposed to address obstacles in FRF-based Bayesian model updating using the Box-Cox transformation and principal component analysis (BCT-PCA). The effectiveness of the proposed methodology is verified in numerical and experimental cases.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2023)
Article
Engineering, Multidisciplinary
Xiukai Yuan, Jian Gu, Shaolong Liu
Summary: This study proposed contribution indexes to measure the sensitivity of failure probability estimate with regards to sample, and derived and analyzed them for four simulation methods. The main differences between these methods lie in the contribution indexes of the safety samples, which are key factors to the efficiency of the methods. Numerical examples were used to validate the findings.
CMES-COMPUTER MODELING IN ENGINEERING & SCIENCES
(2021)
Proceedings Paper
Automation & Control Systems
X. K. Yuan, B. Chen, B. Q. Zhang
INTERNATIONAL CONFERENCE ON COMPUTER INFORMATION AND AUTOMATION ENGINEERING
(2018)
Article
Engineering, Mechanical
R. Allahvirdizadeh, A. Andersson, R. Karoumi
Summary: The operational safety of high-speed trains on ballasted bridges relies on preventing ballast destabilization. This study explores the impact of epistemic uncertainties on the system using ISRA. Neglecting these uncertainties can lead to overestimation of permissible train speeds and reduced system safety.
PROBABILISTIC ENGINEERING MECHANICS
(2024)
Article
Engineering, Mechanical
Lujie Shi, Leila Khalij, Christophe Gautrelet, Chen Shi, Denis Benasciutti
Summary: This study proposes an innovative Two-phase method based on the Langlie method and the D-optimality criterion to overcome the intrinsic shortcomings of the staircase method used in estimating the fatigue limit distribution. Through simulation-based study, it is demonstrated that the proposed method improves the estimation performance for the mean and standard deviation of the fatigue limit distribution.
PROBABILISTIC ENGINEERING MECHANICS
(2024)
Article
Engineering, Mechanical
Axay Thapa, Atin Roy, Subrata Chakraborty
Summary: This article compares different metamodeling approaches for reliability analysis of tunnels to evaluate their performance. The study found that Kriging and support vector regression models perform well in estimating the reliability of underground tunnels.
PROBABILISTIC ENGINEERING MECHANICS
(2024)
Article
Engineering, Mechanical
Jiaqi Wang, Zhenzhou Lu, Lu Wang
Summary: This paper proposes an efficient method to estimate the FP-GS using reliability updating, avoiding the time-consuming double-loop structure analysis. By utilizing the likelihood function and adaptive Kriging model, the unconditional FP and all conditional FPs can be estimated simultaneously.
PROBABILISTIC ENGINEERING MECHANICS
(2024)
Article
Engineering, Mechanical
Jiaxu Li, Ming Liu, Xu Yan, Qianting Yang
Summary: Wind pressure is essential for architectural design, and this study found that using different probabilistic distribution models can improve the accuracy of reference wind pressure calculation. In the research conducted in Liaoning Province, the extreme value type III model and moment method achieved the best fit. Additionally, probability density functions for wind speed and wind direction were established for further analysis of wind pressure.
PROBABILISTIC ENGINEERING MECHANICS
(2024)
Article
Engineering, Mechanical
Yufan Cheng, Xinchen Zhuang, Tianxiang Yu
Summary: This paper proposes a time-dependent kinematic reliability analysis method that takes into account the truncated random variables and joint clearances, effectively addressing the issues of dimension variables and correlation between joint clearance variables. The proposed method transforms time-dependent reliability into time-independent reliability, greatly reducing computational complexity and obtaining upper and lower bounds of failure probability.
PROBABILISTIC ENGINEERING MECHANICS
(2024)
