Experimental and numerical investigation of RC frames strengthened with a hybrid seismic retrofit system

Nguyen-Vu Luat , Hongseok Lee , Jiuk Shin , Ji-Hun Park , Tae-Sang Ahn , Kihak Lee

Abstract

This paper presents experimental and numerical investigations of a new seismic enhancement method for existing reinforced concrete (RC) frames by using an external sub-structure, the hybrid seismic retrofit method (HSRM) system. This retrofit system is an H-shaped frame bolt-connected to an existing RC frame with an infilled-concrete layer between their gaps. Two RC frames were built, one with and one without HSRM, and tested under cyclic loading. The experimental findings showed that the retrofitted RC frame was superior to the non-retrofitted specimen in terms of initial stiffness, peak load, and energy dissipation capacity. A numerical simulation using a commercial program was employed for verification with the experiments. The results obtained from the simulations were consistent with those from the experiments, indicating the finite element (FE) models can simulate the seismic behaviors of bare RC frame and retrofitted RC frame using HSRM.

Key Words

cyclic loading; FEM; LS-DYNA; retrofitted RC frame; seismic retrofit method

Address

Nguyen-Vu Luat — 1)Deep Learning Architecture Research Center, Department of Architectural Engineering, Sejong University, 209 Neungdong-ro, Gwangjin-gu, Seoul 05006, South Korea 2)Institute of Postgraduate and Short-term training, Mien Dong Innovative Technology University, Thong Nhat, Dong Nai 76400, Vietnam
Hongseok Lee — Deep Learning Architecture Research Center, Department of Architectural Engineering, Sejong University, 209 Neungdong-ro, Gwangjin-gu, Seoul 05006, South Korea
Jiuk Shin — Department of Architectural Engineering, Gyeongsang National University, Jinju, 52828, South Korea
Ji-Hun Park — Division of Architecture and Urban Design, Incheon National University, 119 Academy-ro, Yeonsu-gu, Incheon 22012, South Korea
Tae-Sang Ahn — Research Center, Korea Disaster Prevention Technology, 6-1 Neejinmal-gil, Docheok-myeon, Gyeonggi-do, South Korea
Kihak Lee — Deep Learning Architecture Research Center, Department of Architectural Engineering, Sejong University, 209 Neungdong-ro, Gwangjin-gu, Seoul 05006, South Korea

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