Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 전종수 | - |
dc.date.accessioned | 2021-07-07T01:55:14Z | - |
dc.date.available | 2021-07-07T01:55:14Z | - |
dc.date.issued | 2020-03 | - |
dc.identifier.citation | ENGINEERING STRUCTURES, v. 207, article no. 110204 | en_US |
dc.identifier.issn | 0141-0296 | - |
dc.identifier.issn | 1873-7323 | - |
dc.identifier.uri | https://www.sciencedirect.com/science/article/pii/S0141029619306200?via%3Dihub | - |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/162694 | - |
dc.description.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. | en_US |
dc.description.sponsorship | This work was supported by the George E. Brown, Jr. Network for Earthquake Engineering Simulation (NEES) Program of the National Science Foundation, USA under Award Number CMMI-1041607. This support is gratefully acknowledged. Any opinions, findings, conclusions or recommendations are those of the authors and do not necessarily reflect the views of other organizations. | en_US |
dc.language.iso | en | en_US |
dc.publisher | ELSEVIER SCI LTD | en_US |
dc.subject | Multi-hazard loads | en_US |
dc.subject | Seismically-vulnerable building frames | en_US |
dc.subject | Artificial neural network model | en_US |
dc.subject | Rapid decision-making approach | en_US |
dc.title | Multi-hazard assessment and mitigation for seismically-deficient RC building frames using artificial neural network models | en_US |
dc.type | Article | en_US |
dc.relation.volume | 207 | - |
dc.identifier.doi | 10.1016/j.engstruct.2020.110204 | - |
dc.relation.page | 1-16 | - |
dc.relation.journal | ENGINEERING STRUCTURES | - |
dc.contributor.googleauthor | Shin, Jiuk | - |
dc.contributor.googleauthor | Scott, David W. | - |
dc.contributor.googleauthor | Stewart, Lauren K. | - |
dc.contributor.googleauthor | Jeon, Jong-Su | - |
dc.relation.code | 2020046147 | - |
dc.sector.campus | S | - |
dc.sector.daehak | COLLEGE OF ENGINEERING[S] | - |
dc.sector.department | DEPARTMENT OF CIVIL AND ENVIRONMENTAL ENGINEERING | - |
dc.identifier.pid | jongsujeon | - |
dc.identifier.orcid | http://orcid.org/0000-0001-6657-7265 | - |
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