摘要
模型不确定性不可避免地影响到数值模型分析精度和可靠性,需要找到一种合适的方法,根据实测数据对模型参数值进行修正。该研究采用结合了过渡马尔科夫链蒙特卡罗(TMCMC)方法的贝叶斯模型修正理论对结构模型参数进行修正。采用Kriging法和多项式混沌展开法(PCE)构造代理模型。将该修正方法应用于两个不同结构复杂度的实例,这两个模型分别代表高维线性模型和非线性模型。在两个实例下验证了代理模型的有效性和准确性,讨论了基于代理模型的修正方法在不同结构复杂度下的优缺点。针对代理模型存在的不足,提出了一种代理集成学习框架进行改进。
Model uncertainty inevitably affects the accuracy and reliability of numerical model-based analysis.It is necessary to obtain an appropriate updating method which could determine the reasonable values of model parameter from measurements.A Bayesian model updating method is proposed combined with the transitional Markov chain Monte Carlo(TMCMC).Kriging predictor and polynomial chaos expansion(PCE)are utilized to construct surrogate models to reduce the computational burden.The proposed model updating methods are applied to two structural examples with different complexity,which stand for the high-dimensional linear model and the high-dimensional nonlinear model,respectively.The validity and accuracy of two surrogate models are investigated in the numerical examples.The advantages and limitations of the surrogate models-based updating approaches are also discussesed for different structural complexity.For the deficiency of surrogate models,it proposes an ensemble learning method to improve the model updating.
作者
林光伟
张熠
LIN Guang-wei;ZHANG Yi(Department of Civil Engineering,Tsinghua University,Beijing 100084,China)
出处
《工程力学》
EI
CSCD
北大核心
2022年第S01期153-157,共5页
Engineering Mechanics
基金
国家自然科学基金项目(51908324,52111540161)。
