TY - JOUR
T1 - 基于 RBF 和主动学习的非概率可靠度求解方法
AU - Jiang, Feng
AU - Li, Huacong
AU - Fu, Jiangfeng
AU - Hong, Linxiong
N1 - Publisher Copyright:
© 2023 AAAS Press of Chinese Society of Aeronautics and Astronautics. All rights reserved.
PY - 2023/1/25
Y1 - 2023/1/25
N2 - When the failure domain and the hyper-ellipsoid uncertainty domain interfere with each other in non-probabilistic reliability analysis, non-probabilistic reliability is more applicable than the non-probabilistic reliability index. To improve the solution efficiency of structural non-probabilistic reliability of the hyper-ellipsoid model, this paper proposes an active learning method to solve non-probabilistic reliability problems. The jackknifing variance of the Radial Basis Function (RBF) model at the unknown point is derived by combining the cross-validation and the jackknifing methods, so as to estimate the uncertainty of the predicted values. To solve the non-probabilistic reliability, the is employed which is based on the variance. Based on the jackknifing variance, non-probabilistic reliability is solved using the active learning function of RBF. An effective convergence criterion is then proposed to terminate the process of active learning of non-probabilistic reliability analysis. Three numerical examples reveal that this method proposed can estimate the exact non-probabilistic reliability value under the condition of less calculation of the limit state function, and has strong applicability in structural non-probabilistic reliability analysis.
AB - When the failure domain and the hyper-ellipsoid uncertainty domain interfere with each other in non-probabilistic reliability analysis, non-probabilistic reliability is more applicable than the non-probabilistic reliability index. To improve the solution efficiency of structural non-probabilistic reliability of the hyper-ellipsoid model, this paper proposes an active learning method to solve non-probabilistic reliability problems. The jackknifing variance of the Radial Basis Function (RBF) model at the unknown point is derived by combining the cross-validation and the jackknifing methods, so as to estimate the uncertainty of the predicted values. To solve the non-probabilistic reliability, the is employed which is based on the variance. Based on the jackknifing variance, non-probabilistic reliability is solved using the active learning function of RBF. An effective convergence criterion is then proposed to terminate the process of active learning of non-probabilistic reliability analysis. Three numerical examples reveal that this method proposed can estimate the exact non-probabilistic reliability value under the condition of less calculation of the limit state function, and has strong applicability in structural non-probabilistic reliability analysis.
KW - active learning
KW - cross-validation
KW - hyper-ellipsoidal model
KW - non-probabilistic reliability analysis
KW - Radial Basis Function (RBF) model
UR - http://www.scopus.com/inward/record.url?scp=85147977501&partnerID=8YFLogxK
U2 - 10.7527/S1000-6893.2022.26667
DO - 10.7527/S1000-6893.2022.26667
M3 - 文章
AN - SCOPUS:85147977501
SN - 1000-6893
VL - 44
JO - Hangkong Xuebao/Acta Aeronautica et Astronautica Sinica
JF - Hangkong Xuebao/Acta Aeronautica et Astronautica Sinica
IS - 2
M1 - 226667
ER -