Time-Dependent Reliability Analysis Based on Point-Evolution Kernel Density Estimation: Comprehensive Approach with Continuous and Shock Deterioration and Maintenance
Publication: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 7, Issue 3
Abstract
Civil infrastructure may degrade due to the adverse effects of continuous damage (e.g., reinforcement corrosion) and sudden shocks (e.g., earthquakes) during its service life. Many studies have been conducted in the field of reliability-informed life-cycle assessment, but there is still a need for a general and efficient method to assess the time-dependent performance of aging structures by considering different deterioration scenarios and maintenance actions in a unified manner. Some of the traditional methods may have difficulties in handling multiple deteriorations, nonlinear models, a large number of uncertainties, scenarios of nondifferentiable performance functions, and combined effects of deterioration and maintenance. This paper develops a novel approach for a time-dependent reliability analysis based on the proposed point-evolution kernel density estimation (PKDE) method and equivalent extreme performance function. The proposed approach allows consideration of various uncertainties (e.g., external loads, deterioration scenarios, and maintenance models) and the associated correlation effects. In the proposed approach, both the progressive deterioration and sudden damages are considered in the modeling of the performance function. Besides, different types of maintenance schemes are assessed. The equivalent performance function is established, and the proposed PKDE method is used to address the first passage problem and nondifferentiable performance function within a time-dependent reliability analysis. An illustrative example is made to demonstrate the feasibility and accuracy of the proposed PKDE method. The computational results using the proposed method are verified by comparison with those from Monte Carlo simulations (MCS).
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request. Specifically, the data used to generate Figs. 6–13 are available upon request.
Acknowledgments
This study has been supported by National Key R&D Program of China (No. 2019YFB1600702), the National Natural Science Foundation of China (Grant No. 51808476), and the Research Grants Council of the Hong Kong Special Administrative Region, China (Nos. T22-502/18-R and PolyU 15219819).
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ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 7 • Issue 3 • September 2021
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© 2021 American Society of Civil Engineers.
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Received: Dec 17, 2020
Accepted: Apr 8, 2021
Published online: Jun 16, 2021
Published in print: Sep 1, 2021
Discussion open until: Nov 16, 2021
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