Multimodal distributions of some physics based model parameters are often encountered in engineering due to different situations such as a change in some environmental conditions, and the presence of some types of damage and nonlinearity. In statistical model updating, for locally identifiable parameters, it can be anticipated that multi-modal posterior distributions would be found. The full characterization of these multi-modal distributions is important as methodologies for structural condition monitoring in structures are frequently based in the comparison of the damaged and healthy models of the structure. The characterization of posterior multi-modal distributions using state-of-the-art sampling techniques would require a large number of simulations of expensive to run physics-based models. Therefore, when a limited number of simulations can be run, as it often occurs in engineering, the traditional sampling techniques would not be able to capture accurately the multimodal distributions. This could potentially lead to large numerical errors when assessing the performance of an engineering structure under uncertainty.
翻译:由于不同的情况,例如某些环境条件的变化,以及存在某些类型的损害和非线性,工程中往往会遇到一些以物理为基础的模型参数的多式分布; 在更新统计模型时,可以预计,对于可在当地识别的参数,会发现多式后部分布; 这些多式分布的全面特征很重要,因为结构结构结构状况监测方法往往以结构结构受损和健康模型的比较为基础; 使用最新取样技术对后部多式分布进行定性需要大量昂贵的模拟,才能运行以物理为基础的模型; 因此,当能够进行数量有限的模拟时,传统的取样技术将无法准确地捕捉到多式分布; 在评估不确定的工程结构的性能时,这可能导致大量数字错误。