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Multi-hazard assessment and mitigation for seismically-deficient RC building frames using artificial neural network models

Title
Multi-hazard assessment and mitigation for seismically-deficient RC building frames using artificial neural network models
Author
전종수
Keywords
Multi-hazard loads; Seismically-vulnerable building frames; Artificial neural network model; Rapid decision-making approach
Issue Date
2020-03
Publisher
ELSEVIER SCI LTD
Citation
ENGINEERING STRUCTURES, v. 207, article no. 110204
Abstract
Non-ductile reinforced concrete building frames have seismic and blast vulnerabilities due to inadequate reinforcement detailing resulting in premature failure. One option to mitigate these vulnerabilities is the installation of a retrofit system on susceptible structures. However, differences in code-defined performance limits depending on loading type may result in a non-conservative retrofit design under multi-hazard loads. This paper presents a rapid tool for multi-hazard assessment and mitigation for the seismically-vulnerable building frames using artificial neural network models, which can rapidly generate large datasets. Using the models, energy-based performance limits for multi-hazard loading are derived, and a rapid decision-making approach for the retrofit design is developed under seismic and blast loads.
URI
https://www.sciencedirect.com/science/article/pii/S0141029619306200?via%3Dihubhttps://repository.hanyang.ac.kr/handle/20.500.11754/162694
ISSN
0141-0296; 1873-7323
DOI
10.1016/j.engstruct.2020.110204
Appears in Collections:
COLLEGE OF ENGINEERING[S](공과대학) > CIVIL AND ENVIRONMENTAL ENGINEERING(건설환경공학과) > Articles
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