To analyze the effect of the region of the model inputs on the model output,a novel concept about contribution to the sample failure probability plot(CSFP) is proposed based on the contribution to the sample mean plot...To analyze the effect of the region of the model inputs on the model output,a novel concept about contribution to the sample failure probability plot(CSFP) is proposed based on the contribution to the sample mean plot(CSM) and the contribution to the sample variance plot(CSV).The CSFP can be used to analyze the effect of the region of the model inputs on the failure probability.After the definition of CSFP,its property and the differences between CSFP and CSV/CSM are discussed.The proposed CSFP can not only provide the information about which input affects the failure probability mostly,but also identify the contribution of the regions of the input to the failure probability mostly.By employing the Kriging model method on optimized sample points,a solution for CSFP is obtained.The computational cost for solving CSFP is greatly decreased because of the efficiency of Kriging surrogate model.Some examples are used to illustrate the validity of the proposed CSFP and the applicability and feasibility of the Kriging surrogate method based solution for CSFP.展开更多
For structural systems with both epistemic and aleatory uncertainties, research on quantifying the contribution of the epistemic and aleatory uncertainties to the failure probability of the systems is conducted. Based...For structural systems with both epistemic and aleatory uncertainties, research on quantifying the contribution of the epistemic and aleatory uncertainties to the failure probability of the systems is conducted. Based on the method of separating epistemic and aleatory uncertainties in a variable, the core idea of the research is firstly to establish a novel deterministic transition model for auxiliary variables, distribution parameters, random variables, failure probability, then to propose the improved importance sampling （IS） to solve the transition model. Furthermore, the distribution parameters and auxiliary variables are sampled simultaneously and independently;therefore, the inefficient sampling procedure with an＇＇inner-loop＇＇ for epistemic uncertainty and an＇＇outer-loop＇＇ for aleatory uncertainty in traditional methods is avoided. Since the proposed method combines the fast convergence of the proper estimates and searches failure samples in the interesting regions with high efficiency, the proposed method is more efficient than traditional methods for the variance-based failure probability sensitivity measures in the presence of epistemic and aleatory uncertainties. Two numerical examples and one engineering example are introduced for demonstrating the efficiency and precision of the proposed method for structural systems with both epistemic and aleatory uncertainties.展开更多
基金supported by the National Natural Science Foundation of China (Grant No. 51175425)the Aviation Foundation (Grant No.2011ZA53015)
文摘To analyze the effect of the region of the model inputs on the model output,a novel concept about contribution to the sample failure probability plot(CSFP) is proposed based on the contribution to the sample mean plot(CSM) and the contribution to the sample variance plot(CSV).The CSFP can be used to analyze the effect of the region of the model inputs on the failure probability.After the definition of CSFP,its property and the differences between CSFP and CSV/CSM are discussed.The proposed CSFP can not only provide the information about which input affects the failure probability mostly,but also identify the contribution of the regions of the input to the failure probability mostly.By employing the Kriging model method on optimized sample points,a solution for CSFP is obtained.The computational cost for solving CSFP is greatly decreased because of the efficiency of Kriging surrogate model.Some examples are used to illustrate the validity of the proposed CSFP and the applicability and feasibility of the Kriging surrogate method based solution for CSFP.
基金supported by the National Natural Science Foundation of China (No. 51175425)the Special Research Fund for the Doctoral Program of Higher Education of China (No. 20116102110003)
文摘For structural systems with both epistemic and aleatory uncertainties, research on quantifying the contribution of the epistemic and aleatory uncertainties to the failure probability of the systems is conducted. Based on the method of separating epistemic and aleatory uncertainties in a variable, the core idea of the research is firstly to establish a novel deterministic transition model for auxiliary variables, distribution parameters, random variables, failure probability, then to propose the improved importance sampling （IS） to solve the transition model. Furthermore, the distribution parameters and auxiliary variables are sampled simultaneously and independently;therefore, the inefficient sampling procedure with an＇＇inner-loop＇＇ for epistemic uncertainty and an＇＇outer-loop＇＇ for aleatory uncertainty in traditional methods is avoided. Since the proposed method combines the fast convergence of the proper estimates and searches failure samples in the interesting regions with high efficiency, the proposed method is more efficient than traditional methods for the variance-based failure probability sensitivity measures in the presence of epistemic and aleatory uncertainties. Two numerical examples and one engineering example are introduced for demonstrating the efficiency and precision of the proposed method for structural systems with both epistemic and aleatory uncertainties.
