Multivariate non-Gaussian process simulation based on HPM-JTM hybrid model

For the simulation of wind field or wind load, the Gaussian assumption is not applicable in some situations. Hence the simulation of non-Gaussian process becomes significant. In order to avoid the iteration and maintain a wide application range, a HPM-JTM hybrid model is proposed based on translation process. During the simulation, the correlation function of the related Gaussian process can be solved by analytical or numerical expression. Through a numerical example, the application of the model is presented. Results show that the model provides a reasonable estimation for the target case, which can be regarded as an appropriate candidate for the simulation of multivariate non-Gaussian process.     

基于随机过程跨越理论的钢轨动力可靠性分析与比较

采用模态分析方法建立车辆-轨道垂向耦合动力计算模型,使用Wilson-θ数值积分方法,得到钢轨在列车荷载作用下的随机振动响应。基于随机过程跨越理论,得到在不同的跨越假设以及极值分布条件下钢轨的动力响应可靠度,并与数值模拟法计算的结果进行比较。结果表明：对于低跨越界限,极值I型分布方法的计算结果与精确值最为接近;而对于高界限,高斯分布法效果最好。     

基于完全概率的随机桁架结构动力响应分析

提出了一种求解随机桁架结构在随机荷载作用下动力响应概率密度函数的方法.通过对随机参数桁架结构的有限元分析,获得了结构的质量阵与刚度阵,利用振型迭加法导出了结构的物理参数、几何参数、外荷载幅值及频率同时具有随机性时,结构动力响应随机变量的表达式,应用随机向量函数的概率分布函数表达式,通过逐步求解的策略,获得结构动力响应的概率密度函数.算例结果与Monte-Carlo模拟法结果比较表明,该方法具有较高的精度及效率.     

An extended CREAM model based on analytic network process under the type-2 fuzzy environment for human reliability analysis in the high-speed train operation

Human error is one of the largest contributing factors to unsafe operation and accidents in high-speed train operation. As a well-known second-generation human reliability analysis (HRA) technique, the cognitive reliability and error analysis method (CREAM) has been introduced to address HRA problems in various fields. Nevertheless, current CREAM models are insufficient to deal with the HRA problem that need to consider the interdependencies between the Common Performance Conditions (CPCs) and determine the weights of these CPCs, simultaneously. Hence, the purpose of this paper is to develop a hybrid HRA model by integrating CREAM, the interval type-2 fuzzy sets, and analytic network process (ANP) to overcome this drawback. Firstly, the interval type-2 fuzzy sets are utilized to express the highly uncertain information of CPCs. Secondly, the ANP is incorporated into the CREAM to depict the interdependencies between the CPCs and determine their weights. Furthermore, human error probability (HEP) can be calculated based on the obtained weights. Finally, an illustrative example of the HRA problem in high-speed train operation is proposed to demonstrate the application and validity of the proposed HRA model. The results indicate that experts prefer to express their preferences by fuzzy sets rather than crisp values, and the interdependences between the CPCs can be better depicted in the proposed model.