An active learning Bayesian ensemble surrogate model for structural reliability analysis
An active learning Bayesian ensemble surrogate model for structural reliability analysis
复制标题
用于结构可靠性分析的主动学习贝叶斯集成代理模型
DOI：
10.1002/qre.3152
复制
发表时间：
2022-06
影响因子：
2.3
通讯作者：
Yizhong Ma
中科院分区：
文献类型：
--
作者：
Tianli Xiao;Chanseok Park;Linhan Ouyang;Yizhong Ma
代理模型已被证明是减轻结构可靠性分析计算负担的有力工具。合适的代理模型可以在有限样本的情况下保证预测精度。然而，传统的单一建模技术由于对物理系统的认识不足而忽略了模型形式的不确定性，导致预测结果不可靠或计算耗时。针对上述不足，提出了一种基于贝叶斯推理框架的主动学习集成代理模型，用于结构可靠性分析。基于得到的预测响应的贝叶斯后验分布，构造了一个学习函数，该函数集成了改进的U函数和设计点之间的距离信息，以顺序地选择下一个点。此外，为了进一步提高计算效率，我们提出了一种根据估计极限状态面的预测不确定性自适应地识别采样区域的方法。通过5个基准测试实例验证了该算法的有效性和高效性。比较结果表明，基于贝叶斯集成代理模型的主动学习可靠性分析方法在预测精度具有竞争力的情况下，大大降低了计算量。以10杆桁架问题为例，与AK-MCS+U、ALR-BPCE和ALR-SVR算法相比，效率分别提高了51.58%、12.78%和25.96%。同时，它的预测精度很高，远远好于ALR-ELSM。此外，卓越的性能在广泛的应用案例中都是稳健的。
Surrogate models have been proven to be powerful tools to alleviate the computational burden of structural reliability analysis. An appropriate surrogate model can guarantee prediction accuracy with limited samples. However, the traditional single modeling technique ignores the model-form uncertainty due to insufficient knowledge of the physical system, leading to unreliable prediction results or time-consuming computation. To overcome the aforementioned deficiencies, an active learning ensemble surrogate model under the framework of Bayesian inference is proposed for structural reliability analysis. Based on the derived Bayesian posterior distribution of the predicted response, a learning function integrating the modified U function and the distance information between design points is developed to sequentially select the next point. Besides, in order to further enhance the computational efficiency, we propose an adaptive method to identify the sampling region according to the prediction uncertainty of the estimated limit state surface. Five benchmark examples are employed to verify the effectiveness and efficiency of the proposed algorithm. Comparison results show that the proposed active learning reliability analysis method based on the Bayesian ensemble surrogate model can greatly reduce the computational expense with a competitive prediction accuracy. Taking the 10-bar truss problem as an example, compared with AK-MCS+U, ALR-Bpce, and ALR-SVR, the improved rate of the proposed method in efficiency is 51.58%, 12.78%, and 25.96%, respectively. Meanwhile, its prediction accuracy is high and much better than ALR-ELSM. In addition, the superior performance is robust in a wide range of application cases.
登录
查看更多内容
微制造过程中基于0-1编程的集成建模
影响因子：
7.9
作者：
Linhan Ouyang;Dequn Zhou;Yizhong Ma;Yiliu Tu
通讯作者：
Yiliu Tu
影响因子：
5.8
作者：
Yan-Gang Zhao;T. Ono
通讯作者：
Yan-Gang Zhao;T. Ono
DOI：
10.1016/j.compchemeng.2017.05.025
发表时间：
2017-11
期刊：
Comput. Chem. Eng.
影响因子：
--
作者：
Haitao Liu;Jianfei Cai;Y. Ong
通讯作者：
Haitao Liu;Jianfei Cai;Y. Ong
DOI：
10.1002/9780470824269
发表时间：
2009-09
期刊：
--
影响因子：
--
作者：
Jie Li;Jianbing Chen
通讯作者：
Jie Li;Jianbing Chen
DOI：
10.1007/s13137-017-0101-z
发表时间：
2017-12
期刊：
GEM - International Journal on Geomathematics
影响因子：
--
作者：
K. Koch
通讯作者：
K. Koch