Structural performance and damage prediction using a novel digital cloning technique

Abstract

This paper presents a novel, mechanics-based framework to create digital twins for earthquake-affected pinched structures, like reinforced concrete framed buildings. It uses robust and accurate structural health monitoring (SHM) results delivered by the hysteresis loop analysis method as an input to create digital twin models to predict nonlinear dynamic responses of previously damaged structures under potential future seismic events. Method validation is implemented using unique real-world data from the Bank of New Zealand (BNZ) building in Wellington, New Zealand, which experienced severe structural damage due to three earthquakes (Events 1, 2, and 3) between 2013 and 2016.Results show the digital twin derived from the SHM results of Event 1 can predict the inter-story displacement of the BNZ building for Events 2 and 3 with average correlation coefficients of ∼0.95 and ∼0.97 between predicted and measured responses, respectively. Moreover, the maximum difference between the measured and predicted peak values of inter-story displacements was 13 mm, a negligible difference in inter-story drift ratio (IDR). These results were accurate and consistent for all stories. Finally, the accuracy of this framework in capturing IDR values makes it a promising tool for assessing potential future structural collapse risk and its consequent financial risks using well-known incremental dynamic analysis methods.