Earthquakes and Structures

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Performance-based optimization of 2D reinforced concrete wall-frames using pushover analysis and ABC optimization algorithm

  • Saba Faghirnejad (Department of Civil, Environmental and Architectural Engineering, The University of Kansas) ;
  • Denise-Penelope N. Kontoni (Department of Civil Engineering, School of Engineering, University of the Peloponnese) ;
  • Mohammad Reza Ghasemi (Department of Civil Engineering, University of Sistan and Baluchestan)
  • Received : 2024.02.25
  • Accepted : 2024.07.22
  • Published : 2024.10.25
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Abstract

Conducting nonlinear pushover analysis typically demands intricate and resource-intensive computational efforts, involving a highly iterative process necessary for meeting both design-defined and requirements of codes in performance-based design. This study presents a computer-based technique for reinforced concrete (RC) buildings, incorporating optimization numerical approaches, optimality criteria and pushover analysis to automatically enhance seismic design performance. The optimal design of concrete beams, columns and shear walls in concrete frames is presented using the artificial bee colony optimization algorithm. The methodology is applied to three frames: a 4-story, an 8-story and a 12-story. These structures are designed to minimize overall weight while satisfying the levels of performance including Life Safety (LS), Collapse Prevention (CP), and Immediate Occupancy (IO). The process involves three main steps: first, optimization codes are implemented in MATLAB software, and the OpenSees software is used for nonlinear static analysis. By solving the optimization problem, several top designs are obtained for each frame and shear wall. Pushover analysis is conducted considering the constraints on relative displacement and plastic hinge rotation based on the nonlinear provisions of the FEMA356 nonlinear provisions to achieve each level of performance. Subsequently, convergence, pushover, and drift history curves are plotted for each frame, and leading to the selection of the best design. The results demonstrate that the algorithm effectively achieves optimal designs with reduced weight, meeting the desired performance criteria.

Keywords

References

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