Details
| Original language | English |
|---|---|
| Article number | 108794 |
| Journal | Reliability Engineering and System Safety |
| Volume | 228 |
| Early online date | 28 Aug 2022 |
| Publication status | Published - Dec 2022 |
Abstract
Performing time-dependent reliability analysis is an effective way to estimate the failure probability of structural system throughout its lifetime. In the engineering practices, uncertain parameters with sufficient sample and limited sample may exist simultaneously. The uncertain parameters with limited sample data are difficult to construct its precise probabilistic characteristics during estimating the accurate time-dependent reliability. To address this issue, this paper first develops a new hybrid time-dependent reliability model involving interval processes. Then, to reduce the high dimensionality, an extension method based on equivalent stochastic process transformation approach is proposed to transform the stochastic processes and the interval processes into corresponding equivalent random variables respectively. In particular, an instantaneous reliability model is constructed to envelope all potential system failures that may occur during the time interval. In order to identify the instantaneous failure surface accurately, an active learning method is proposed based on the deep neural network model and the weighted sampling method. With the constructed deep neural network model, the new hybrid time-dependent reliability can be evaluated by performing the Monte Carlo Sampling. Three numerical examples are used to verify the accuracy and efficiency of the proposed method.
Keywords
- Active learning, Deep neural network, Hybrid uncertain model, Time-dependent reliability analysis, Weighted sampling.
ASJC Scopus subject areas
- Engineering(all)
- Safety, Risk, Reliability and Quality
- Engineering(all)
- Industrial and Manufacturing Engineering
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In: Reliability Engineering and System Safety, Vol. 228, 108794, 12.2022.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - An efficient reliability analysis method for structures with hybrid time-dependent uncertainty
AU - Zhang, Kun
AU - Chen, Ning
AU - Zeng, Peng
AU - Liu, Jian
AU - Beer, Michael
N1 - Funding Information: The paper is supported by the National Natural Science Foundation of China (Grant No. 51905162 ), the Foundation for Innovative Research Groups of the National Natural Science Foundation of China (Grant No. 51621004 ) and the Natural Science Foundation of Hunan Province (Grant No. 2022JJ30132 ). The author would also like to thank reviewers for their valuable suggestions.
PY - 2022/12
Y1 - 2022/12
N2 - Performing time-dependent reliability analysis is an effective way to estimate the failure probability of structural system throughout its lifetime. In the engineering practices, uncertain parameters with sufficient sample and limited sample may exist simultaneously. The uncertain parameters with limited sample data are difficult to construct its precise probabilistic characteristics during estimating the accurate time-dependent reliability. To address this issue, this paper first develops a new hybrid time-dependent reliability model involving interval processes. Then, to reduce the high dimensionality, an extension method based on equivalent stochastic process transformation approach is proposed to transform the stochastic processes and the interval processes into corresponding equivalent random variables respectively. In particular, an instantaneous reliability model is constructed to envelope all potential system failures that may occur during the time interval. In order to identify the instantaneous failure surface accurately, an active learning method is proposed based on the deep neural network model and the weighted sampling method. With the constructed deep neural network model, the new hybrid time-dependent reliability can be evaluated by performing the Monte Carlo Sampling. Three numerical examples are used to verify the accuracy and efficiency of the proposed method.
AB - Performing time-dependent reliability analysis is an effective way to estimate the failure probability of structural system throughout its lifetime. In the engineering practices, uncertain parameters with sufficient sample and limited sample may exist simultaneously. The uncertain parameters with limited sample data are difficult to construct its precise probabilistic characteristics during estimating the accurate time-dependent reliability. To address this issue, this paper first develops a new hybrid time-dependent reliability model involving interval processes. Then, to reduce the high dimensionality, an extension method based on equivalent stochastic process transformation approach is proposed to transform the stochastic processes and the interval processes into corresponding equivalent random variables respectively. In particular, an instantaneous reliability model is constructed to envelope all potential system failures that may occur during the time interval. In order to identify the instantaneous failure surface accurately, an active learning method is proposed based on the deep neural network model and the weighted sampling method. With the constructed deep neural network model, the new hybrid time-dependent reliability can be evaluated by performing the Monte Carlo Sampling. Three numerical examples are used to verify the accuracy and efficiency of the proposed method.
KW - Active learning
KW - Deep neural network
KW - Hybrid uncertain model
KW - Time-dependent reliability analysis
KW - Weighted sampling.
UR - http://www.scopus.com/inward/record.url?scp=85138757022&partnerID=8YFLogxK
U2 - 10.1016/j.ress.2022.108794
DO - 10.1016/j.ress.2022.108794
M3 - Article
AN - SCOPUS:85138757022
VL - 228
JO - Reliability Engineering and System Safety
JF - Reliability Engineering and System Safety
SN - 0951-8320
M1 - 108794
ER -