Machine learning (ML) approaches have gained increasing attention in the structural engineering field to evaluate structural performance using structural health monitoring (SHM) data. Supervised ML approaches can accelerate the learning process by using labeled training datasets to map an input to output dataset. But, SHM data are not informative to drive a mapping function to determine the real-world performance of large-scale complex structures in particular for future events. To leverage a framework for evaluating the system-level structural performance, this study couples supervised ML approaches with an advanced finite element (FE) model considering pre- and post-event model validation and updating. A well-instrumented system experiencing multiple seismic events is employed as a case study to demonstrate the proposed framework. An FE model of the instrumented system is created and validated using pre-event SHM datasets. Numerical data obtained from the FE model are used for datasets to develop ML prediction models, which are then validated by a post-event SHM dataset. Eight popular ML algorithms are examined and compared to shed light on the effectiveness of the ML algorithms for the proposed framework. The case study results indicate that the Random Forests and Neural Network algorithms provide better estimation for the structural system. The results also imply the need of post-event updating for numerical models used in the case study.

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