# Structural Health Monitoring Using Machine Learning and Cumulative Absolute Velocity Features
## 5 Discussions and Conclusions  
In this paper, a rapid damage assessment methodology is presented that uses ML algorithms with single dimension CAV based features. A computer experiment was performed to identify the appropriate features and the ML algorithm using data from a simulated SDOF system. Results from this experiment revealed that \(CAV\) and \(R_{CAV}\) pair as the ideal feature combination. Not only this combination produced the highest accuracy with all ML tools but also produced the highest accuracy when tested against a dataset different from the training set. Moreover, among the considered four ML algorithms, OLR performed the best for its generalization capabilities. The presented rapid damage assessment methodology was subsequently applied on two MDOF models and one instrumented building. For the two MDOF models, namely MDOF-US and MDOF-NS, detections of the damage state and location were assessed. The study showed that the proposed method correctly detected the worst damage state with about 91% accuracy for both MDOF models when tested with TE-1. Moreover, it achieved damage state accuracy of 84% and location accuracy of 97.5% for MDOF-US and damage state accuracy of 97% and location accuracy of 95% for MDOF-NS when tested with TE-2. This shows that the proposed method can detect and assess damage even if datasets come from distributions different from the training dataset. However, from the recall values, it was evident that the method was more successful in detecting undamaged and major damage classes than moderate or minor damage classes. The proposed method did exceedingly well in damage location detection even when the locations were fairly distributed among different stories. For the instrumented building, the training sets came from the analytical models of the lateral force resisting systems of the building. Test sets were the recorded earthquake data of two perpendicular directions. Results showed that damage detection accuracy was 90% and 80% for the two test sets and their corresponding models using OLR. Further investigation of the accuracy values revealed the possibility of undetected damages in one of the earthquake events that made the structure vulnerable to the subsequent damaging earthquake events. Rapid and accurate monitoring of structures is vital for decision making tasks regarding re-occupancy, emergency response, and future use of the structure following an earthquake event. The techniques reported in this study achieve these tasks via instantaneous use of the response data (simulated and/and measured) to locate and assess the structural integrity. The major challenge in the application of the proposed technique comes from the lack of recorded data as well as the lack of detailed analytical models. A resilient performance in the face of natural hazards can be achieved when all buildings in a community are instrumented to record and transfer strong motion data and have fairly detailed analytical models, i.e., when _digital twins_ are developed and validated. The current developments in sensor technologies, high-performance computing, and wireless data communication open the door to resolve this challenge. Conceptualization & methodology, S.M. and K.M.M. ; Software & validation, S.M. and K.M.M. ; Formal analysis & interpretation, S.M. and K.M.M. ; Data curation, S.M. ; Original draft preparation, S.M. and K.M.M. ; Supervision & final revision, K.M.M. ; Funding acquisition, K.M.M. Both authors have read and agreed to the published version of the manuscript. This research is mainly supported by the California Department of Conservation, California Geological Survey, Strong Motion Instrumentation Program agreements 1017-562 and 1019-016; [PERSON] Chair of Civil Engineering, University of California, Berkeley; and California Department of Transportation (Caltarns) for \"Bridge Rapid Assessment Center for Extreme Events (BRACE2)\" project (TO 001), part of the PEER-Bridge Program agreement 65A0774. **Informed Consent Statement:** Not applicable.