In structural reliability analysis,simulation methods are widely used.The statistical characteristics of failure probability estimate of these methods have been well investigated.In this study,the sensitivities of the...In structural reliability analysis,simulation methods are widely used.The statistical characteristics of failure probability estimate of these methods have been well investigated.In this study,the sensitivities of the failure probability estimate and its statistical characteristics with regard to sample,called‘contribution indexes’,are proposed to measure the contribution of sample.The contribution indexes in four widely simulation methods,i.e.,Monte Carlo simulation(MCS),importance sampling(IS),line sampling(LS)and subset simulation(SS)are derived and analyzed.The proposed contribution indexes of sample can provide valuable information understanding the methods deeply,and enlighten potential improvement of methods.It is found that the main differences between these investigated methods lie in the contribution indexes of the safety samples,which are the main factors to the efficiency of the methods.Moreover,numerical examples are used to validate these findings.展开更多
Importance analysis quantifies the critical degree of individual component. Compared with the traditional binary state system,importance analysis of the multi-state system is more aligned with the practice. Because th...Importance analysis quantifies the critical degree of individual component. Compared with the traditional binary state system,importance analysis of the multi-state system is more aligned with the practice. Because the multi-valued decision diagram( MDD) can reflect the relationship between the components and the system state bilaterally, it was introduced into the reliability calculation of the multi-state system( MSS). The building method,simplified criteria,and path search and probability algorithm of MSS structure function MDD were given,and the reliability of the system was calculated. The computing methods of importance based on MDD and direct partial logic derivatives( DPLD) were presented. The diesel engine fuel supply system was taken as an example to illustrate the proposed method. The results show that not only the probability of the system in each state can be easily obtained,but also the influence degree of each component and its state on the system reliability can be obtained,which is conducive to the condition monitoring and structure optimization of the system.展开更多
基于采用 von 的直接力量计算，协定强调失败标准的可靠性分析的一个方法这里被介绍。轮船壳的短术语紧张分布结构的部件通过导致波浪的紧张历史的统计分析被识别，由短术语的加权的求和的长期的分布拉紧分布。导致波浪的长期的紧张分...基于采用 von 的直接力量计算，协定强调失败标准的可靠性分析的一个方法这里被介绍。轮船壳的短术语紧张分布结构的部件通过导致波浪的紧张历史的统计分析被识别，由短术语的加权的求和的长期的分布拉紧分布。导致波浪的长期的紧张分发与静止的水紧张被相结合。反应紧张的极端紧张分发被联合紧张的统计分析获得。可靠性分析的限制状态功能基于 von 协定压力失败标准，包括由于材料的质量的相关无常和模型无常。可靠性索引用在可靠性上有效果的每个变量的形式和敏感分析被计算也被讨论。展开更多
During the life of an offshore structure, its structural strength declines due to various kinds of damages related to the time factor. In this paper, four major kinds of damages, including damages caused by fatigue, d...During the life of an offshore structure, its structural strength declines due to various kinds of damages related to the time factor. In this paper, four major kinds of damages, including damages caused by fatigue, dent, corrosion and marine life, are discussed. Based on these analyses, formulas for the evaluation of the damaged structure reliability are derived. Furthermore the computer program ISM for the analysis of structural reliability is developed by the use of Advanced First Order Second Moment method and Monte-Carlo Importance Sampling method. The reliability of a turbular joint and a beam are studied as numerical examples. The results show that the theory and the analysis method given in this paper are reasonable and effective.展开更多
Based on the observation of importance sampling and second order information about the failure surface of a structure, an importance sampling region is defined in V-space which is obtained by rotating a U-space at the...Based on the observation of importance sampling and second order information about the failure surface of a structure, an importance sampling region is defined in V-space which is obtained by rotating a U-space at the point of maximum likelihood. The sampling region is a hyper-ellipsoid that consists of the sampling ellipse on each plane of main curvature in V-space. Thus, the sampling probability density function can be constructed by the sampling region center and ellipsoid axes. Several examples have shown the efficiency and generality of this method.展开更多
Monte Carlo Simulations(MCS),commonly used for reliability analysis,require a large amount of data points to obtain acceptable accuracy,even if the Subset Simulation with Importance Sampling(SS/IS)methods are used.The...Monte Carlo Simulations(MCS),commonly used for reliability analysis,require a large amount of data points to obtain acceptable accuracy,even if the Subset Simulation with Importance Sampling(SS/IS)methods are used.The Second Order Reliability Method(SORM)has proved to be an excellent rapid tool in the stochastic analysis of laminated composite structures,when compared to the slower MCS techniques.However,SORM requires differentiating the performance function with respect to each of the random variables involved in the simulation.The most suitable approach to do this is to use a symbolic solver,which renders the simulations very slow,although still faster than MCS.Moreover,the inability to obtain the derivative of the performance function with respect to some parameters,such as ply thickness,limits the capabilities of the classical SORM.In this work,a Neural Network-Based Second Order Reliability Method(NNBSORM)is developed to replace the finite element algorithm in the stochastic analysis of laminated composite plates in free vibration.Because of the ability to obtain expressions for the first and second derivatives of the NN system outputs with respect to any of its inputs,such as material properties,ply thicknesses and orientation angles,the need for using a symbolic solver to calculate the derivatives of the performance function no longer exists.The proposed approach is accordingly much faster,and easily allows for the consideration of ply thickness uncertainty.The present analysis showed that dealing with ply thicknesses as random variables results in 37%increase in the laminate’s probability of failure.展开更多
Simulation based structural reliability analysis suffers from a heavy computational burden, as each sample needs to be evaluated on the performance function, where structural analysis is performed. To alleviate the co...Simulation based structural reliability analysis suffers from a heavy computational burden, as each sample needs to be evaluated on the performance function, where structural analysis is performed. To alleviate the computational burden, related research focuses mainly on reduction of samples and application of surrogate model, which substitutes the performance function. However,the reduction of samples is achieved commonly at the expense of loss of robustness, and the construction of surrogate model is computationally expensive. In view of this, this paper presents a robust and efficient method in the same direction. The present method uses radial-based importance sampling (RBIS) to reduce samples without loss of robustness. Importantly, Kriging is fully used to efficiently implement RBIS. It not only serves as a surrogate to classify samples as we all know, but also guides the procedure to determine the optimal radius, with which RBIS would reduce samples to the highest degree. When used as a surrogate, Kriging is established through active learning, where the previously evaluated points to determine the optimal radius are reused. The robustness and efficiency of the present method are validated by five representative examples, where the present method is compared mainly with two fundamental reliability methods based on active learning Kriging.展开更多
基金NSAF(Grant No.U1530122)the Aeronautical Science Foundation of China(Grant No.ASFC-20170968002)the Fundamental Research Funds for the Central Universities of China(XMU,20720180072).
文摘In structural reliability analysis,simulation methods are widely used.The statistical characteristics of failure probability estimate of these methods have been well investigated.In this study,the sensitivities of the failure probability estimate and its statistical characteristics with regard to sample,called‘contribution indexes’,are proposed to measure the contribution of sample.The contribution indexes in four widely simulation methods,i.e.,Monte Carlo simulation(MCS),importance sampling(IS),line sampling(LS)and subset simulation(SS)are derived and analyzed.The proposed contribution indexes of sample can provide valuable information understanding the methods deeply,and enlighten potential improvement of methods.It is found that the main differences between these investigated methods lie in the contribution indexes of the safety samples,which are the main factors to the efficiency of the methods.Moreover,numerical examples are used to validate these findings.
基金National Natural Science Foundation of China(No.61164009)the Science and Technology Research Project,Department of Education of Jiangxi Province,China(No.GJJ14420)Natural Science Foundation of Jiangxi Province,China(No.20132BAB206026)
文摘Importance analysis quantifies the critical degree of individual component. Compared with the traditional binary state system,importance analysis of the multi-state system is more aligned with the practice. Because the multi-valued decision diagram( MDD) can reflect the relationship between the components and the system state bilaterally, it was introduced into the reliability calculation of the multi-state system( MSS). The building method,simplified criteria,and path search and probability algorithm of MSS structure function MDD were given,and the reliability of the system was calculated. The computing methods of importance based on MDD and direct partial logic derivatives( DPLD) were presented. The diesel engine fuel supply system was taken as an example to illustrate the proposed method. The results show that not only the probability of the system in each state can be easily obtained,but also the influence degree of each component and its state on the system reliability can be obtained,which is conducive to the condition monitoring and structure optimization of the system.
文摘基于采用 von 的直接力量计算，协定强调失败标准的可靠性分析的一个方法这里被介绍。轮船壳的短术语紧张分布结构的部件通过导致波浪的紧张历史的统计分析被识别，由短术语的加权的求和的长期的分布拉紧分布。导致波浪的长期的紧张分发与静止的水紧张被相结合。反应紧张的极端紧张分发被联合紧张的统计分析获得。可靠性分析的限制状态功能基于 von 协定压力失败标准，包括由于材料的质量的相关无常和模型无常。可靠性索引用在可靠性上有效果的每个变量的形式和敏感分析被计算也被讨论。
文摘During the life of an offshore structure, its structural strength declines due to various kinds of damages related to the time factor. In this paper, four major kinds of damages, including damages caused by fatigue, dent, corrosion and marine life, are discussed. Based on these analyses, formulas for the evaluation of the damaged structure reliability are derived. Furthermore the computer program ISM for the analysis of structural reliability is developed by the use of Advanced First Order Second Moment method and Monte-Carlo Importance Sampling method. The reliability of a turbular joint and a beam are studied as numerical examples. The results show that the theory and the analysis method given in this paper are reasonable and effective.
文摘Based on the observation of importance sampling and second order information about the failure surface of a structure, an importance sampling region is defined in V-space which is obtained by rotating a U-space at the point of maximum likelihood. The sampling region is a hyper-ellipsoid that consists of the sampling ellipse on each plane of main curvature in V-space. Thus, the sampling probability density function can be constructed by the sampling region center and ellipsoid axes. Several examples have shown the efficiency and generality of this method.
文摘Monte Carlo Simulations(MCS),commonly used for reliability analysis,require a large amount of data points to obtain acceptable accuracy,even if the Subset Simulation with Importance Sampling(SS/IS)methods are used.The Second Order Reliability Method(SORM)has proved to be an excellent rapid tool in the stochastic analysis of laminated composite structures,when compared to the slower MCS techniques.However,SORM requires differentiating the performance function with respect to each of the random variables involved in the simulation.The most suitable approach to do this is to use a symbolic solver,which renders the simulations very slow,although still faster than MCS.Moreover,the inability to obtain the derivative of the performance function with respect to some parameters,such as ply thickness,limits the capabilities of the classical SORM.In this work,a Neural Network-Based Second Order Reliability Method(NNBSORM)is developed to replace the finite element algorithm in the stochastic analysis of laminated composite plates in free vibration.Because of the ability to obtain expressions for the first and second derivatives of the NN system outputs with respect to any of its inputs,such as material properties,ply thicknesses and orientation angles,the need for using a symbolic solver to calculate the derivatives of the performance function no longer exists.The proposed approach is accordingly much faster,and easily allows for the consideration of ply thickness uncertainty.The present analysis showed that dealing with ply thicknesses as random variables results in 37%increase in the laminate’s probability of failure.
基金supported by the National Natural Science Foundation of China (Grant No. 11421091)the Fundamental Research Funds for the Central Universities (Grant No. HIT.MKSTISP.2016 09)
文摘Simulation based structural reliability analysis suffers from a heavy computational burden, as each sample needs to be evaluated on the performance function, where structural analysis is performed. To alleviate the computational burden, related research focuses mainly on reduction of samples and application of surrogate model, which substitutes the performance function. However,the reduction of samples is achieved commonly at the expense of loss of robustness, and the construction of surrogate model is computationally expensive. In view of this, this paper presents a robust and efficient method in the same direction. The present method uses radial-based importance sampling (RBIS) to reduce samples without loss of robustness. Importantly, Kriging is fully used to efficiently implement RBIS. It not only serves as a surrogate to classify samples as we all know, but also guides the procedure to determine the optimal radius, with which RBIS would reduce samples to the highest degree. When used as a surrogate, Kriging is established through active learning, where the previously evaluated points to determine the optimal radius are reused. The robustness and efficiency of the present method are validated by five representative examples, where the present method is compared mainly with two fundamental reliability methods based on active learning Kriging.
