A multi-objective design method for seismic retrofitting of existing reinforced concrete frames using pin-supported rocking walls

Abstract

Over the past several decades, a variety of technical ways have been developed in seismic retrofitting of existing reinforced concrete frames (RFs). Among them, pin-supported rocking walls (PWs) have received much attentions to researchers recently. However, it is still a challenge that how to determine the stiffness demand of PWs and assign the value of the drift concentration factor (DCF) for entire systems rationally and efficiently. In this paper, a design method has been exploited for seismic retrofitting of existing RFs using PWs (RF-PWs) via a multi-objective evolutionary algorithm. Then, the method has been investigated and verified through a practical project. Finally, a parametric analysis was executed to exhibit the strengths and working mechanism of the multi-objective design method. To sum up, the findings of this investigation show that the method furnished in this paper is feasible, functional and can provide adequate information for determining the stiffness demand and the value of the DCFfor PWs. Furthermore, it can be applied for the preliminary design of these kinds of structures.

Graphical abstract

Keywords

pin-supported rocking wall / reinforced concrete frame / seismic retrofit / stiffness demand / drift concentration factor / multi-objective design / genetic algorithm / Pareto optimal solution

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Acknowledgements

The authors are grateful for the financial supports from the Scientific Research Fund of Institute of Engineering Mechanics, China Earthquake Administration (Nos. 2019D12 and 2019D11); Open Foundation of State Key Laboratory of Disaster Reduction in Civil Engineering, Tongji University in China (No. SLDRCE19-01); Foundation of Public Welfare Technology Research Project of Zhejiang Province in China (No. LGF20E080013); Natural Science Foundation of Zhejiang Province, China (No. LY22E080003); Fundamental Research Fund for the Provincial Universities of Zhejiang (No. SJLZ2022003) and Foundation of Public Welfare Technology Research Project of Ningbo in China, (Nos. 2022S170, 2022S179).

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