In densely populated urban areas, reinforced concrete residential buildings with an open first floor and closed upper floors are preferred to meet user demands, resulting in significant vertical stiffness irregularities. These vertical stiffness irregularities promote the development of a soft-story mechanism, leading to concentrated damage on the first floor during seismic events. To mitigate seismic damage caused by the soft-story mechanism, stiffness-based retrofit strategies are favored, and it is crucial to determine an economically optimal level of retrofitting. This study aims to establish optimal seismic retrofit strategies by evaluating the seismic losses of buildings before and after stiffness-based retrofitting. An equivalent single-degree-of-freedom model is established to describe the seismic response of a multi-degree-of-freedom model, allowing for seismic demand analysis. By convolving the seismic loss function with the hazard curve, the annual expected loss (EAL) of the building is calculated to assess the economic losses. The results show that stiffness-based retrofitting increases first-story lateral stiffness by 20-40%, enhancing structural seismic performance, but also results in a rise in EAL compared to the as-built state, indicating lower cost-effectiveness from an economic perspective. The research concludes that retrofit options that increase first-story lateral stiffness by at least 60% are more appropriate for reducing financial losses.

저층 건축물의 횡-비틀림 거동은 고차모드 효과를 증폭시킬 수 있으며, 내진성능평가 시 관련 기준은 고차모드 지배 구조물에 대해 비선형정적해석과 함께 선형동적해석을 추가로 수행하도록 규정하고 있다. 선형동적절차에는 상당한 안전계수가 적용되므로, 이는 과도한 내진보강설계로 이어질 수 있다. 이를 방지하기 위해 엔지니어들은 내진보강 시 고차모드 효과를 줄이기 위해 시행착오법을 사용해 왔다. 그러나 시행착오법에는 많은 시간과 노력이 소요되며, 결정된 보강안이 최적인지 확인하기 어렵다. 본 연구는 저층 건 축물의 수학적 모델을 수립하고 응답스펙트럼해석을 통해 고차모드 효과에 비틀림이 독립적으로 미치는 영향을 파악하였다. 이를 바탕으로 효율적인 내진보강 설계를 위해 활용될 수 있는 도표와 절차를 제시하였다. 제시된 절차를 통해 최소한의 내진보강으로 횡- 비틀림 거동하는 저층 건축물의 고차모드 효과를 효율적으로 감소시킬 수 있음을 확인하였다.

Many school buildings are vulnerable to earthquakes because they were built before mandatory seismic design was applied. This study uses machine learning to develop an algorithm that rapidly constructs an optimal reinforcement scheme with simple information for non-ductile reinforced concrete school buildings built according to standard design drawings in the 1980s. We utilize a decision tree (DT) model that can conservatively predict the failure type of reinforced concrete columns through machine learning that rapidly determines the failure type of reinforced concrete columns with simple information, and through this, a methodology is developed to construct an optimal reinforcement scheme for the confinement ratio (CR) for ductility enhancement and the stiffness ratio (SR) for stiffness enhancement. By examining the failure types of columns according to changes in confinement ratio and stiffness ratio, we propose a retrofit scheme for school buildings with masonry walls and present the maximum applicable stiffness ratio and the allowable range of stiffness ratio increase for the minimum and maximum values of confinement ratio. This retrofit scheme construction methodology allows for faster construction than existing analysis methods.

Existing reinforced concrete buildings with seismically deficient details have premature failure under earthquake loads. The fiber-reinforced polymer column jacket enhances the lateral resisting capacities with additional confining pressures. This paper aims to quantify the retrofit effect varying the confinement and stiffness-related parameters under three earthquake scenarios and establish the retrofit strategy. The retrofit effects were estimated by comparing energy demands between non-retrofitted and retrofitted conditions. The retrofit design parameters are determined considering seismic hazard levels to maximize the retrofit effects. The critical parameters of the retrofit system were determined by the confinement-related parameters at moderate and high seismic levels and the stiffness-related parameters at low seismic levels.