Output-only methods based on machine/deep-learning algorithms are highly practical approaches for timely detecting potential damages in civil structures as they directly employ measured vibration signals but do not require exact knowledge of input loading nor the service interruption for manual inspection. However, there is no one-size-fits-all model that is optimal for all problems in different perspectives; hence, it is necessary to discover the advantages as well as drawbacks of different models, then leverage these understandings to select the most appropriate model for specific structures in reality. Therefore, this study extensively compares various machine learning-based methods ranging from relatively simple ones such as Naïve Bayes to complex ones such as the extreme boosting tree-based ensemble model. The comparison results include various aspects such as model complexity, training time, detection accuracy, and inference time. The results show that for the bagging ensemble model, random forest achieves the highest detection accuracy, and the non-parameter KNN model has a good balance between accuracy and model complexity.
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