论文标题
增强贝叶斯模型使用不完整的模态信息使用并行，互动和自适应马尔可夫链
Enhanced Bayesian Model Updating with Incomplete Modal Information Using Parallel, Interactive and Adaptive Markov Chains
论文作者
论文摘要
有限的元素模型更新具有挑战性，因为1）问题通常是不确定的，而测量值有限和/或不完整； 2）参数的许多组合可能产生相对于实际测量的响应； 3）不可避免地存在不确定性。这项研究的目的是通过统计推断来利用计算智能，以促进增强的，概率的有限元模型使用不完整的模态响应测量来更新。这个新框架是建立在通过优化的有效逆识别的基础上的，而贝叶斯推论则用于说明不确定性的影响。为了克服计算成本障碍，我们采用马尔可夫链蒙特卡洛（MCMC）来表征目标功能/分布。我们没有在常规的贝叶斯方法中使用单一马尔可夫链，而是开发了一种具有多个平行，互动和自适应马尔可夫链的新抽样理论，并将其纳入贝叶斯推断中。这可以利用这些马尔可夫连锁店的集体力量来实现对多个本地最佳Opta的同时搜索。所需的马尔可夫链的数量及其各自的初始模型参数将通过基于蒙特卡洛模拟的样品预筛查，然后进行K-均值聚类分析来自动确定。这些增强功能可以有效地解决有限元模型更新中上述挑战。该框架的有效性是通过案例研究系统地证明的。
Finite element model updating is challenging because 1) the problem is oftentimes underdetermined while the measurements are limited and/or incomplete; 2) many combinations of parameters may yield responses that are similar with respect to actual measurements; and 3) uncertainties inevitably exist. The aim of this research is to leverage upon computational intelligence through statistical inference to facilitate an enhanced, probabilistic finite element model updating using incomplete modal response measurement. This new framework is built upon efficient inverse identification through optimization, whereas Bayesian inference is employed to account for the effect of uncertainties. To overcome the computational cost barrier, we adopt Markov chain Monte Carlo (MCMC) to characterize the target function/distribution. Instead of using single Markov chain in conventional Bayesian approach, we develop a new sampling theory with multiple parallel, interactive and adaptive Markov chains and incorporate into Bayesian inference. This can harness the collective power of these Markov chains to realize the concurrent search of multiple local optima. The number of required Markov chains and their respective initial model parameters are automatically determined via Monte Carlo simulation-based sample pre-screening followed by K-means clustering analysis. These enhancements can effectively address the aforementioned challenges in finite element model updating. The validity of this framework is systematically demonstrated through case studies.
