Multi-hazard assessment and mitigation for seismically-deficient RC building frames using artificial neural network models
- Authors
- Shin, Jiuk; Scott, David W.; Stewart, Lauren K.; Jeon, Jong-Su
- Issue Date
- Mar-2020
- Publisher
- ELSEVIER SCI LTD
- Keywords
- Multi-hazard loads; Seismically-vulnerable building frames; Artificial neural network model; Rapid decision-making approach
- Citation
- ENGINEERING STRUCTURES, v.207, pp.1 - 16
- Indexed
- SCIE
SCOPUS
- Journal Title
- ENGINEERING STRUCTURES
- Volume
- 207
- Start Page
- 1
- End Page
- 16
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/10653
- DOI
- 10.1016/j.engstruct.2020.110204
- ISSN
- 0141-0296
- 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.
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