The Experts below are selected from a list of 321 Experts worldwide ranked by ideXlab platform
B.f. Song - One of the best experts on this subject based on the ideXlab platform.
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A technique for computing Failure Probability of a structure using importance sampling
Computers & Structures, 1997Co-Authors: B.f. SongAbstract:Abstract In this paper, based on the Probability formula of the union of events and the concept of the importance sampling, a technique for computing structural System Failure Probability is developed. In Melcher's method, all importance sampling functions corresponding to the structural significance Failure modes should be combined by some criteria, and they would affect the precision of results or computational efficiency. In this paper, the developed technique is based on the multiple simulation using every importance sampling function in turn, the criterion for combination is not necessary, so, the shortcomings of Melcher's method is overcome. Several examples illustrate the developed technique of this paper.
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A numerical integration method in affine space and a method with high accuracy for computing structural System reliability
Computers & Structures, 1992Co-Authors: B.f. SongAbstract:Abstract In this paper, a numerical integration method in affine space for computing structural System Failure Probability is presented. This method has the advantage over the method in orthogonal space in that the integration grid can be easily generated because of the very simple integration domain. Furthermore, by using it to compute the two- and three-order joint Failure probabilities, the computational accuracy of Feng's method for combining System Failure Probability is increased.
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System reliability analysis and optimization of stiffened panels under compression
Reliability Engineering & System Safety, 1992Co-Authors: B.f. Song, Y. S. FengAbstract:Abstract A System reliability analysis method and an optimization method of stiffened panels are presented in this paper. The correlations among the System resistances of Failure modes are considered to increase computational accuracy. By taking the allowable structural System Failure Probability as a constraint, the mathematical model of structural optimization is established and the feasible direction method is used to solve it. The effectiveness and efficiency are shown by the illustrative examples.
Glaucio H. Paulino - One of the best experts on this subject based on the ideXlab platform.
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Parameter Sensitivity of System Reliability Using Sequential Compounding Method
Structural Safety, 2015Co-Authors: Junho Chun, Junho Song, Glaucio H. PaulinoAbstract:Abstract Computation of sensitivities of the ‘System’ Failure Probability with respect to various parameters is essential in reliability based design optimization (RBDO) and uncertainty/risk management of a complex engineering System. The System Failure event is defined as a logical function of multiple component events representing Failure modes, locations or time points. Recently, the sequential compounding method (SCM) was developed for efficient calculations of the probabilities of large-size, general System events for a wide range of correlation properties. To facilitate the use of SCM in RBDO and uncertainty/risk management under a constraint on the System Failure Probability, a method, termed as Chun–Song–Paulino (CSP) method, is developed in this paper to compute parameter sensitivities of System Failure Probability using SCM. For a parallel or series System, the derivative of the System Failure Probability with respect to the reliability index is analytically derived at the last step of the sequential compounding. For a general System, the sensitivity of the Probability of the set involving the component of interest and the sensitivity of the System Failure Probability with respect to the super-component representing the set are computed respectively using the CSP method and combined by the chain-rule. The CSP method is illustrated by numerical examples, and successfully tested by examples covering a wide range of System event types, reliability indices, number of components, and correlation properties. The method is also applied to compute the sensitivity of the first-passage Probability of a building structure under stochastic excitations, modeled by use of finite elements.
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Single-loop System reliability-based topology optimization considering statistical dependence between limit-states
Structural and Multidisciplinary Optimization, 2011Co-Authors: Tam H. Nguyen, Junho Song, Glaucio H. PaulinoAbstract:This paper presents a single-loop algorithm for System reliability-based topology optimization (SRBTO) that can account for statistical dependence between multiple limit-states, and its applications to computationally demanding topology optimization (TO) problems. A single-loop reliability-based design optimization (RBDO) algorithm replaces the inner-loop iterations to evaluate probabilistic constraints by a non-iterative approximation. The proposed single-loop SRBTO algorithm accounts for the statistical dependence between the limit-states by using the matrix-based System reliability (MSR) method to compute the System Failure Probability and its parameter sensitivities. The SRBTO/MSR approach is applicable to general System events including series, parallel, cut-set and link-set Systems and provides the gradients of the System Failure Probability to facilitate gradient-based optimization. In most RBTO applications, probabilistic constraints are evaluated by use of the first-order reliability method for efficiency. In order to improve the accuracy of the reliability calculations for RBDO or RBTO problems with high nonlinearity, we introduce a new single-loop RBDO scheme utilizing the second-order reliability method and implement it to the proposed SRBTO algorithm. Moreover, in order to overcome challenges in applying the proposed algorithm to computationally demanding topology optimization problems, we utilize the multiresolution topology optimization (MTOP) method, which achieves computational efficiency in topology optimization by assigning different levels of resolutions to three meshes representing finite element analysis, design variables and material density distribution respectively. The paper provides numerical examples of two- and three-dimensional topology optimization problems to demonstrate the proposed SRBTO algorithm and its applications. The optimal topologies from deterministic, component and System RBTOs are compared with one another to investigate the impact of optimization schemes on final topologies. Monte Carlo simulations are also performed to verify the accuracy of the Failure probabilities computed by the proposed approach.
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Single-Loop System Reliability-Based Design Optimization Using Matrix-Based System Reliability Method: Theory and Applications
Journal of Mechanical Design, 2009Co-Authors: Tam H. Nguyen, Junho Song, Glaucio H. PaulinoAbstract:This paper proposes a single-loop System reliability-based design optimization (SRBDO) approach using the recently developed matrix-based System reliability (MSR) method. A single-loop method was employed to eliminate the inner-loop of SRBDO that evaluates probabilistic constraints. The MSR method enables us to compute the System Failure Probability and its parameter sensitivities efficiently and accurately through convenient matrix calculations. The SRBDO/MSR approach proposed in this paper is applicable to general Systems including series, parallel, cut-set, and link-set System events. After a brief overview on SRBDO algorithms and the MSR method, the SRBDO/MSR approach is introduced and demonstrated by three numerical examples. The first example deals with the optimal design of a combustion engine, in which the Failure is described as a series System event. In the second example, the cross-sectional areas of the members of a statically indeterminate truss structure are determined for minimum total weight with a constraint on the Probability of collapse. In the third example, the redistribution of the loads caused by member Failures is considered for the truss System in the second example. The results based on different optimization approaches are compared for further investigation. Monte Carlo simulation is performed in each example to confirm the accuracy of the System Failure Probability computed by the MSR method.
Liang Gao - One of the best experts on this subject based on the ideXlab platform.
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Sampling-based System reliability-based design optimization using composite active learning Kriging
Computers & Structures, 2020Co-Authors: Jinhao Zhang, Mi Xiao, Liang GaoAbstract:Abstract This paper proposes a sampling-based System reliability-based design optimization (SRBDO) method with local approximation of constraints. To enhance the optimization efficiency of SRBDO problems with time-consuming constraints, Kriging metamodels are employed to replace the true constraint functions. A new composite active learning strategy based on the possibility of correctly predicting the state of the cut-set System is developed to locally approximate constraints. Furthermore, to ensure the accuracy of the System reliability analysis at the final SRBDO solution, the Kriging update in the developed strategy is terminated by quantifying the influence of the Kriging uncertainty on the prediction of the System Failure Probability and the confidence that the solution satisfies the prescribed System Failure Probability. This approach can avoid the unnecessary burden of Kriging construction during System reliability analysis at intermediate solutions far from the final solution. Based on the updated Kriging metamodel, the System Failure Probability of constraints is estimated by Monte Carlo simulation, and its partial derivative is calculated by stochastic sensitivity analysis. The performance of the proposed method is tested and verified by using four examples. Compared with the existing methods, the proposed method has high computational accuracy and efficiency for solving SRBDO problems.
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A composite-projection-outline-based approximation method for System reliability analysis with hybrid uncertainties
Reliability Engineering & System Safety, 2020Co-Authors: Jinhao Zhang, Liang Gao, Mi XiaoAbstract:Abstract This paper investigates the System reliability analysis under random and interval variables (SRA-RI). When performance functions in SRA-RI are replaced by surrogate models, it is found that the composite projection outlines on the composite limit-state surface should be well approximated so as to accurately assess System Failure Probability bounds. Then a composite-projection-outline-based active learning Kriging (CPOK) method is proposed in this paper. To refine the approximated composite projection outlines, three System learning functions are defined in CPOK for parallel, series and mixed Systems, respectively. Based on these three functions, new points around the composite projection outlines are sequentially selected and used for the update of Kriging models. Meanwhile, prediction uncertainties of Kriging models are quantified and used for terminating the update of Kriging models. Finally, System Failure Probability bounds are evaluated by Monte Carlo simulation. The advantages of CPOK are validated by four numerical examples and a piezoelectric energy harvester example.
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A System active learning Kriging method for System reliability-based design optimization with a multiple response model
Reliability Engineering & System Safety, 2020Co-Authors: Mi Xiao, Jinhao Zhang, Liang GaoAbstract:Abstract This paper proposes a System active learning Kriging (SALK) method to handle System reliability-based design optimization (SRBDO) problems, where responses of all constraints at an input can be obtained simultaneously by running a multiple response model. In SALK, to select update points around the limit-state surfaces, three new System active learning functions are respectively defined for parallel, series and combined Systems. The confidence interval of estimation of System Failure Probability at intermediate SRBDO solutions is considered in the stopping condition of Kriging update to reduce unnecessary update points used for refining the region far from the final SRBDO solution. Based on updated Kriging models, System Failure Probability is estimated by Monte Carlo simulation (MCS), and its partial derivative with respect to random variables is calculated by stochastic sensitivity analysis. The efficiency of the proposed SALK method for SRBDO is validated by four examples, including a power harvester design. The results indicate that SALK can locally approximate the limit-state surfaces around the final SRBDO solution and efficiently reduce the computational cost on the refinement of the region far from the final SRBDO solution.
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EEK-SYS: System reliability analysis through estimation error-guided adaptive Kriging approximation of multiple limit state surfaces
Reliability Engineering & System Safety, 2020Co-Authors: Chen Jiang, Liang Gao, Haobo Qiu, Dapeng Wang, Zan Yang, Liming ChenAbstract:Abstract In order to approximate the multiple limit state functions for different Failure events, the active learning Kriging model proposed for component reliability analysis has been extended to System reliability analysis. Meanwhile, many efficient sampling strategies have been applied to reduce the high computational burden. However, these strategies meet a challenge in wasting some training points and terminating the training process inappropriately, since they do not directly relate to the estimation error of System Failure Probability. To address the challenge, this work proposes an estimation error-guided adaptive Kriging method. As Kriging prediction may be inaccurate before being well trained, the predicted System Failure Probability may deviate from the true result. To quantify this estimation error, the true number of Failure points is approximated by adding the number of predicted Failure points and the number of wrongly classified points. Since it is impossible to learn the exact number of wrongly classified points, its confidence interval is derived based on the Probability of making wrong state classification. Subsequently, the refinement of Kriging is achieved by using the Probability to identify new points and using the estimation error to determine the termination, which has been demonstrated by three different cases.
Junho Song - One of the best experts on this subject based on the ideXlab platform.
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Parameter Sensitivity of System Reliability Using Sequential Compounding Method
Structural Safety, 2015Co-Authors: Junho Chun, Junho Song, Glaucio H. PaulinoAbstract:Abstract Computation of sensitivities of the ‘System’ Failure Probability with respect to various parameters is essential in reliability based design optimization (RBDO) and uncertainty/risk management of a complex engineering System. The System Failure event is defined as a logical function of multiple component events representing Failure modes, locations or time points. Recently, the sequential compounding method (SCM) was developed for efficient calculations of the probabilities of large-size, general System events for a wide range of correlation properties. To facilitate the use of SCM in RBDO and uncertainty/risk management under a constraint on the System Failure Probability, a method, termed as Chun–Song–Paulino (CSP) method, is developed in this paper to compute parameter sensitivities of System Failure Probability using SCM. For a parallel or series System, the derivative of the System Failure Probability with respect to the reliability index is analytically derived at the last step of the sequential compounding. For a general System, the sensitivity of the Probability of the set involving the component of interest and the sensitivity of the System Failure Probability with respect to the super-component representing the set are computed respectively using the CSP method and combined by the chain-rule. The CSP method is illustrated by numerical examples, and successfully tested by examples covering a wide range of System event types, reliability indices, number of components, and correlation properties. The method is also applied to compute the sensitivity of the first-passage Probability of a building structure under stochastic excitations, modeled by use of finite elements.
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System reliability analysis using dominant Failure modes identified by selective searching technique
Reliability Engineering & System Safety, 2013Co-Authors: Dong-seok Kim, Junho Song, Hyun-moo KohAbstract:Abstract The Failure of a redundant structural System is often described by innumerable System Failure modes such as combinations or sequences of local Failures. An efficient approach is proposed to identify dominant Failure modes in the space of random variables, and then perform System reliability analysis to compute the System Failure Probability. To identify dominant Failure modes in the decreasing order of their contributions to the System Failure Probability, a new simulation-based selective searching technique is developed using a genetic algorithm. The System Failure Probability is computed by a multi-scale matrix-based System reliability (MSR) method. Lower-scale MSR analyses evaluate the probabilities of the identified Failure modes and their statistical dependence. A higher-scale MSR analysis evaluates the System Failure Probability based on the results of the lower-scale analyses. Three illustrative examples demonstrate the efficiency and accuracy of the approach through comparison with existing methods and Monte Carlo simulations. The results show that the proposed method skillfully identifies the dominant Failure modes, including those neglected by existing approaches. The multi-scale MSR method accurately evaluates the System Failure Probability with statistical dependence fully considered. The decoupling between the Failure mode identification and the System reliability evaluation allows for effective applications to larger structural Systems.
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Single-loop System reliability-based topology optimization considering statistical dependence between limit-states
Structural and Multidisciplinary Optimization, 2011Co-Authors: Tam H. Nguyen, Junho Song, Glaucio H. PaulinoAbstract:This paper presents a single-loop algorithm for System reliability-based topology optimization (SRBTO) that can account for statistical dependence between multiple limit-states, and its applications to computationally demanding topology optimization (TO) problems. A single-loop reliability-based design optimization (RBDO) algorithm replaces the inner-loop iterations to evaluate probabilistic constraints by a non-iterative approximation. The proposed single-loop SRBTO algorithm accounts for the statistical dependence between the limit-states by using the matrix-based System reliability (MSR) method to compute the System Failure Probability and its parameter sensitivities. The SRBTO/MSR approach is applicable to general System events including series, parallel, cut-set and link-set Systems and provides the gradients of the System Failure Probability to facilitate gradient-based optimization. In most RBTO applications, probabilistic constraints are evaluated by use of the first-order reliability method for efficiency. In order to improve the accuracy of the reliability calculations for RBDO or RBTO problems with high nonlinearity, we introduce a new single-loop RBDO scheme utilizing the second-order reliability method and implement it to the proposed SRBTO algorithm. Moreover, in order to overcome challenges in applying the proposed algorithm to computationally demanding topology optimization problems, we utilize the multiresolution topology optimization (MTOP) method, which achieves computational efficiency in topology optimization by assigning different levels of resolutions to three meshes representing finite element analysis, design variables and material density distribution respectively. The paper provides numerical examples of two- and three-dimensional topology optimization problems to demonstrate the proposed SRBTO algorithm and its applications. The optimal topologies from deterministic, component and System RBTOs are compared with one another to investigate the impact of optimization schemes on final topologies. Monte Carlo simulations are also performed to verify the accuracy of the Failure probabilities computed by the proposed approach.
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Single-Loop System Reliability-Based Design Optimization Using Matrix-Based System Reliability Method: Theory and Applications
Journal of Mechanical Design, 2009Co-Authors: Tam H. Nguyen, Junho Song, Glaucio H. PaulinoAbstract:This paper proposes a single-loop System reliability-based design optimization (SRBDO) approach using the recently developed matrix-based System reliability (MSR) method. A single-loop method was employed to eliminate the inner-loop of SRBDO that evaluates probabilistic constraints. The MSR method enables us to compute the System Failure Probability and its parameter sensitivities efficiently and accurately through convenient matrix calculations. The SRBDO/MSR approach proposed in this paper is applicable to general Systems including series, parallel, cut-set, and link-set System events. After a brief overview on SRBDO algorithms and the MSR method, the SRBDO/MSR approach is introduced and demonstrated by three numerical examples. The first example deals with the optimal design of a combustion engine, in which the Failure is described as a series System event. In the second example, the cross-sectional areas of the members of a statically indeterminate truss structure are determined for minimum total weight with a constraint on the Probability of collapse. In the third example, the redistribution of the loads caused by member Failures is considered for the truss System in the second example. The results based on different optimization approaches are compared for further investigation. Monte Carlo simulation is performed in each example to confirm the accuracy of the System Failure Probability computed by the MSR method.
Paolo Vestrucci - One of the best experts on this subject based on the ideXlab platform.
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On System Failure Probability density function
Reliability Engineering & System Safety, 2007Co-Authors: Luca Campioni, Paolo VestrucciAbstract:The assessment of the System unreliability is usually accomplished through well-known tools such as block diagram, fault tree, Monte Carlo and others. These methods imply the knowledge of the Failure Probability density function of each component “k†(pdf pk). For this reason, possibly, the System Failure Probability density function (psys) has never been explicitly derived.
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On System Failure Probability density function
Reliability Engineering & System Safety, 2007Co-Authors: Luca Campioni, Paolo VestrucciAbstract:Abstract The assessment of the System unreliability is usually accomplished through well-known tools such as block diagram, fault tree, Monte Carlo and others. These methods imply the knowledge of the Failure Probability density function of each component “k” (pdf p k ). For this reason, possibly, the System Failure Probability density function ( p sys ) has never been explicitly derived. The present paper fills this gap achieving an enlightening formulation which explicitly gives p sys as the sum of (positive) terms representing the complete set of transitions leading the System from an operating to a failed configuration, due to the Failure of “a last” component. As a matter of fact, these are all the independent sequences leading the System to the Failure. In our opinion, this formulation is important from both methodological and practical point of views. From the methodological one, a clear insight of the System-vs-components behaviors can be grasped and, in general, the explicit link between p sys and p k seems to be a notable result. From a practical point of view, p sys allows a rigorous derivation of Monte Carlo algorithms and suggests a Systematic tool for investigating the System Failure sequences.
