A data‐driven framework for near real‐time and robust damage diagnosis of building structures

Rapid condition monitoring of structural health is essential for post-earthquake safety assessment. Therefore, information about damage after extreme events could be of great value in resilient communities. This paper proposes a robust framework for the identification of the existence, probable location, and severity of damage using cumulative intensity-based damage features. Taken into account the seismic hazard uncertainties and the undesirable consequences of misclassification in data-driven methods, an objective function based on the confusion score matrix is optimized. This process can enhance the reliability of the prediction model in terms of two conflicting criteria, namely, the general accuracy and conservativeness. Support vector machines are utilized for the task of damage classification where Bayesian optimization is used to select the proper damage-sensitive input features and hyperparameters. A three-story reinforced concrete (RC) moment frame is designed and modeled in OpenSees to examine the performance of the proposed framework. For this purpose, 5,400 incrementally scaled nonlinear time history analyses are conducted considering 180 ground motions. Two different approaches are introduced to distort signals to simulate measurement noise. The robustness of models is also investigated with respect to different objective functions. Furthermore, in an independent experimental case study, the framework is evaluated on a dataset obtained from 44 shake table trials on a three-story RC frame with masonry infill. Given the results obtained from the two case studies, it is shown that the proposed framework is capable of robust and reliable identification of damage in near real-time whereas the concepts of hazard uncertainty and misclassification consequences are properly considered.