结构随机分析的概率与复合重要度及其求解

针对基于方差方法的重要度信息量不足而矩独立重要度计算量大、求解难的缺点,将响应量的偏度和蜂度信息引入灵敏度分析中,定义了概率重要度和复合重要度,用于衡量基本变量对响应量分布形状的主要影响趋势及在该影响趋势下的影响程度.同时针对传统方法求解矩的困难,将控制理论中的状态依存参数模型运用于本文所提重要度的求解,极大程度地化筒了计算.最后,通过算例中与基于方差方法的重要度与矩独立重要度的比较,可知本文所提重要度涵盖了基于方差方法重要度和矩独立重要度的主要信息,说明本文重要度的合理性和正确性,算例与Monte Carlo数值模拟方法的比较,表明状态依存参数模型求解方法具有较好的计算精度和效率.

Probability and compound uncertainty importance measures of structure stochastic analysis and their computational method

Based on the lack of the variance-based importance measure and the moment-independent one,probability and composite uncertainty importance measures were proposed by imitating the failure probability in the reliability analysis.These indicators looked at the input influence on the shift trend and shift degree of the output distribution shape with the model output skewness and kurtosis.Besides,it was used the state dependent parameter model replacing traditional methods to estimate the moments in these uncertainty importance measures.It reduced the computational cost effectively.Finally,the results of some examples showed the validity and feasibility of the present measure compared with the variance-based importance measure and the moment-independent one.The examples also showed the calculating efficiency and precision of the state dependent parameter model method in comparison with Monte-Carlo simulation.