In structural health monitoring, correct classification of a building’s inelastic deformation mode (i.e., total-yield or soft-story) is needed for accurate safety evaluations. However, sensors are usually not placed on all floors in most applications, making inelastic deformation mode classification difficult. In this study, features based on plastic displacements and sensor location are proposed for training and evaluating inelastic deformation mode classification models. The importance of the newly proposed features was compared against other features proposed in literature based on peak floor acceleration and velocity response, cumulative absolute velocity and jerk. A large building response database was created from numerical simulations of a wide range of reinforced concrete frame structures exhibiting different inelastic deformation modes for evaluating feature importance. It was found that the newly proposed features ranked highly when applying the Minimum Redundancy Maximum Relevancy algorithm to the response database compared to past features. Furthermore, a k-Nearest Neighbor classification model trained using a feature set containing the proposed features and building-level ductility response resulted in a more accurate model compared to only using existing features (misclassification rate of 10 % versus 29 %). These results demonstrate the suitability of the proposed features for training and evaluating building inelastic deformation mode classification models.
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