Abstract
Few studies have been conducted to assess post-earthquake performance of structures using vibration measurements. This paper presents system identification and finite element modeling of an 18-story apartment building that was damaged during the 2015 Gorkha earthquake in Nepal. In July 2016, a few months after the earthquake, the authors visited the building and collected its ambient acceleration response. The recorded data are analyzed and the modal parameters of the structure are identified using an output-only system identification method. A linear finite element model of the building is also developed based on the geometry of the building and its material properties to estimate numerically its dynamic characteristics. The identified modal parameters are compared to those of the model to identify possible shortcomings of the modeling and identification approaches. The identified natural frequencies and mode shapes of the first two vibration modes are in good agreement with the model.
Highlights
This paper presents data collection, structural identification, and finite element (FE) modeling of an 18-story reinforced concrete (RC) building in Kathmandu that was damaged during the Gorkha earthquake
This can be caused by the fact that three closely spaced modes with natural frequencies around 0.6 Hz are identified as one mode with an inflated damping ratio to account for the three peaks
This study investigates post-earthquake dynamic performance of an 18-story building in Nepal that was partially damaged during the 2015 Gorkha earthquake and its subsequent aftershocks
Summary
This paper presents data collection, structural identification, and finite element (FE) modeling of an 18-story reinforced concrete (RC) building in Kathmandu that was damaged during the Gorkha earthquake. Moaveni et al (2014) compared the performance of three OMA methods (SSI, NExT-ERA, and frequency domain decomposition) for system identification of a seven-story shear wall structure using experimental and numerical data. Kim et al (2014) used Principal Component Analysis (PCA) of each vertex and its eight nearest neighboring vertices to estimate surface normals and compared each surface normal to a normal vector of the plane fitted to the entire data set This proposed workflow could successfully detect the damaged areas; the method is limited to detection of shallow defects in small-sized planar surfaces. Guldur and Hajjar (2014) performed the damage detection of point clouds by using various methods including the variation of surface normals This method can detect defects, construction of the reference vectors requires numerous lengthy processes (segmentation, curvature computation, and identifying the member geometry). The predicted inter-story drift ratios are compared with level of observed damage and lidar data at different stories along the height of the building
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