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• W4295981419 abstract "Due to deep uncertainties associated with climate change and socioeconomic growth, managing bridge networks faces the challenge to perform optimization for different scenarios. There exist a large number of scenarios when various sources of uncertainties, such as population growth and the increasing magnitude and frequency of natural hazards due to climate change, are compounded. Traditionally, scenarios are analyzed sequentially. However, when optimization for one single scenario is time-consuming, only a limited number of scenarios can be considered. To accelerate scenario analysis, this paper proposes a novel scheme through knowledge transfer between scenarios. Specifically, after finishing the optimization of a certain number of scenarios, the analyses of any new scenarios are accelerated by utilizing the knowledge obtained from optimization of previous scenarios. To implement the novel scheme, a proper definition of similar scenarios for adaptation of bridge networks under deep uncertainties is first given to stipulate the situation when knowledge transfer can occur. Then an approach based on surrogate modeling and meta-learning is used to realize the concept of knowledge transfer and perform the optimization. A bridge network is used as an illustrative example to demonstrate the computational efficiency of the proposed novel scheme." @default.
• W4295981419 created "2022-09-16" @default.
• W4295981419 creator A5048125635 @default.
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• W4295981419 date "2023-01-01" @default.
• W4295981419 modified "2023-10-14" @default.
• W4295981419 title "Efficient scenario analysis for optimal adaptation of bridge networks under deep uncertainties through knowledge transfer" @default.
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• W4295981419 doi "https://doi.org/10.1016/j.strusafe.2022.102278" @default.
• W4295981419 hasPublicationYear "2023" @default.
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