Rapid postearthquake safety evaluation of buildings using sparse acceleration measurements

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Abstract
After a seismic event, buildings need to be inspected to confirm their safety prior to reoccupation. As such, the rapid evaluation of the condition of individual buildings is important for minimizing disruption to lives and business. However, traditional manual inspection by experts is laborious and time-consuming. Structural health monitoring provides the potential to accelerate the required evaluation. This article proposes a cost-effective approach for rapid safety evaluation of buildings after seismic events using sparse acceleration measurements. First, a damage-sensitive feature is defined that can be used to infer the condition of buildings. Herein, the maximum interstory drift angle is proposed as a reliable damage index to classify the safety of buildings after seismic events. A convolutional neural network is then employed to uncover the complex relationship between the damage-sensitive features and the building condition. A five-story steel building is considered to validate the proposed approach. First, a three-dimensional nonlinear model of the building is created. To generate the required training data, a simplified nonlinear model is developed, along with a corresponding linear model, as use of the three-dimensional model is too computationally expensive. The training data for the convolutional neural network incorporates uncertainties in both the analysis model and the ground motion. Initial evaluation is conducted using the simplified nonlinear model, while final validation of the proposed approach is performed using the results of the three-dimensional nonlinear analysis model subjected to historical earthquakes. The results demonstrate the ability of the proposed approach to accommodate differences between the in-situ structure and the analysis model, as well as the efficacy of this approach for rapid postearthquake safety evaluation of buildings.

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