TY - JOUR
T1 - An accelerated active learning Kriging model with the distance-based subdomain and a new stopping criterion for reliability analysis
AU - Zhang, Yu
AU - Dong, You
AU - Xu, Jun
N1 - Funding Information:
This study has been supported by the National Natural Science Foundation of China (Grant No. 52078448 ), and the Research Grants Council of the Hong Kong Special Administrative Region, China (No. PolyU 15219819 and PolyU 15221521 ).
Publisher Copyright:
© 2022 Elsevier Ltd
PY - 2023/3
Y1 - 2023/3
N2 - Reliability analysis for computationally expensive models is a challenging problem. Monte Carlo simulation is commonly employed in conjunction with the active learning assisted Kriging model (AK-MCS), which can significantly reduce the number of model evaluations. However, updating a Kriging model with numerous samples is time-consuming, especially for the small failure probability estimation. To alleviate the computational effort induced by the active learning process, the distance-based subdomain is first developed to select the candidate points in the vicinity of the limit state surface. Accordingly, a Kriging model is trained within distance-based subdomains and Kriging predictions on the whole population can be avoided. On the other hand, to further mitigate the computational effort caused by time-demanding model evaluations, a new stopping criterion is formulated based on the expected upper bound of the relative error of failure probability. Furthermore, a reasonable target threshold for the new stopping criterion is suggested based on the quantification of the effect of the selected threshold on the relative error of failure probability. Two analytical performance functions and three numerical models including a 61-bar truss, a reinforced concrete planar frame and a bolted steel beam–column joint are investigated to demonstrate the efficiency and accuracy of the proposed approach.
AB - Reliability analysis for computationally expensive models is a challenging problem. Monte Carlo simulation is commonly employed in conjunction with the active learning assisted Kriging model (AK-MCS), which can significantly reduce the number of model evaluations. However, updating a Kriging model with numerous samples is time-consuming, especially for the small failure probability estimation. To alleviate the computational effort induced by the active learning process, the distance-based subdomain is first developed to select the candidate points in the vicinity of the limit state surface. Accordingly, a Kriging model is trained within distance-based subdomains and Kriging predictions on the whole population can be avoided. On the other hand, to further mitigate the computational effort caused by time-demanding model evaluations, a new stopping criterion is formulated based on the expected upper bound of the relative error of failure probability. Furthermore, a reasonable target threshold for the new stopping criterion is suggested based on the quantification of the effect of the selected threshold on the relative error of failure probability. Two analytical performance functions and three numerical models including a 61-bar truss, a reinforced concrete planar frame and a bolted steel beam–column joint are investigated to demonstrate the efficiency and accuracy of the proposed approach.
KW - Active learning
KW - Distance-based subdomain
KW - Kriging model
KW - Reliability analysis
KW - Small failure probability
UR - http://www.scopus.com/inward/record.url?scp=85144063843&partnerID=8YFLogxK
U2 - 10.1016/j.ress.2022.109034
DO - 10.1016/j.ress.2022.109034
M3 - Journal article
AN - SCOPUS:85144063843
SN - 0951-8320
VL - 231
JO - Reliability Engineering and System Safety
JF - Reliability Engineering and System Safety
M1 - 109034
ER -