Structural Identification of an 18-Story RC Building in Nepal Using Post-Earthquake Ambient Vibration and Lidar Data

Abstract

Few studies have been conducted to systematically assess post-earthquake condition of structures using vibration measurements. This paper presents system identification and finite element (FE) modeling of an 18-story apartment building that was damaged during the 2015 Gorkha earthquake and its aftershocks in Nepal. In June 2015, a few months after the earthquake, the authors visited the building and recorded the building's 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 FE model of the building is also developed to estimate numerically its dynamic properties. 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 for two of the three closely spaced vibration modes in the lower frequency range of interest (0.2-1.0 Hz) are in good agreement with the numerical model. The model is used to estimate the response of the building to the nearby recorded ground motion due to earthquake and the main aftershock. The maximum drift ratios are compared to the observed damage in the building and surface defects detected and quantified by the lidar scans as the research team performed a series of light detection and ranging (lidar) scans from interior of selected floors to document the damage patterns along the height of the building.

System identification and modeling of a dynamically tested and gradually damaged 10‐story reinforced concrete building

“…Typically, the modes of buildings can be identified with a model order of 50 or less . However, the first mode of the 10‐story building is only identified when a much higher system order is considered.…”

Identifying Collapsed Buildings Using Post-Earthquake Satellite Imagery and Convolutional Neural Networks: A Case Study of the 2010 Haiti Earthquake

“…In addition, LiDAR has the ability to work at day and night, even in adverse conditions, such as in poor illumination or through clouds and smoke. A number of studies have used post-even LiDAR imageries to detect building damage [20][21][22][23]. There are also some studies that combined optical imagery with SAR imagery.…”

Seismic Assessment of Non-conforming Infilled RC Buildings Using IDA Procedures

“…At the same time, a large number of analytical investigations of the behaviour of masonry infilled RC building structures have been pursued, at different modelling scales and levels of complexity, in order to predict the effect of masonry infills on infilled frame response and failure (Smith and Carter, 1969;Dhanasekar and Page, 1986;Fardis and Calvi, 1995;Crisafulli, 1997;Kappos and Ellul, 2000;Chrysostomou et al, 2002;Fajfar, 2002, 2008a,b;Repapis et al, 2006b;Borzi et al, 2008;Bakas et al, 2009;Asteris and Cotsovos, 2012;Chrysostomou and Asteris, 2012;Ellul and D'Ayala, 2012;Haldar and Singh, 2012;Lagaros, 2012;Vougioukas, 2012;Sarhosis et al, 2014;Zeris, 2014;Jeon et al, 2015;Bolea, 2016;Dumaru et al, 2016;Furtado et al, 2016;Morfidis and Kostinakis, 2017). More recently, with the advancement of testing and data acquisition hardware, together with the evolution of fast and efficient algorithms for data handling techniques, these two approaches above are jointly pursued in full scale field testing vis-à-vis the dynamic model identification (OMA) in order to establish the dynamic characteristics of full scale structures under excitation (Rainieri, 2008;Yu et al, 2017).…”