Optimal FRP Jacket Placement in RC Frame Structures Towards a Resilient Seismic Design
AffiliationUniv Arizona, Dept Civil & Architectural Engn & Mech
particle swarm optimization
MetadataShow full item record
CitationMahdavi, G.; Nasrollahzadeh, K.; Hariri-Ardebili, M.A. Optimal FRP Jacket Placement in RC Frame Structures Towards a Resilient Seismic Design. Sustainability 2019, 11, 6985.
RightsCopyright © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
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AbstractThis paper proposes an optimal plan for seismically retrofitting reinforced concrete (RC) frame structures. In this method, the columns are wrapped by fiber-reinforced polymer (FRP) layers along their plastic hinges. This technique enhances their ductility and increases the resiliency of the structure. Two meta-heuristic algorithms (i.e., genetic algorithm and particle swarm optimization) are adopted for this purpose. The number of FRP layers is assumed to be the design variable. The objective of the optimization procedure was to provide a uniform usage of plastic hinge rotation capacity for all the columns, while minimizing the consumption of the FRP materials. Toward this aim, a single objective function containing penalty terms is introduced. The seismic performance of the case study RC frame was assessed by means of nonlinear pushover analyses, and the capacity of the plastic hinge rotation for FRP-confined columns was evaluated at the life safety performance level. The proposed framework was then applied to a non-ductile low-rise RC frame structure. The optimal retrofit scheme for the frame was determined, and the capacity curve, inter-story drift ratios, and fragility functions were computed and compared with alternative retrofit schemes. The proposed algorithm offers a unique technique for the design of more resilient structures.
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VersionFinal published version
Except where otherwise noted, this item's license is described as Copyright © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).