## 6 Conclusions  
The critical issues in the data anomaly detection were investigated with the rule-based and the CNN-based approaches. The rules for the seven classes of data anomaly used in [26] were defined for the rule-based classification. As the two classes _Trend_ and _Drift_ were not seen to be strictly separated from _Normal_, _Trend-ambiguous_ and _Drift-ambiguous_ were further defined. The acceleration data obtained from the Hwatae Bridge were labeled using the defined rules and were used to train the CNN model for classification. The following issues in the data anomaly classification were identified in the process of applying rule-based and CNN-based classifications:  
1. Measured data have intrinsic ambiguity, as we defined two ambiguous classes. Thus, both determining a perfect rule that can separate the data without ambiguity and labeling such data for training the CNN model were seen to be impossible.
2. Important information can be lost during data conversion into images, as this conversion generates non-original data anomalies due to quantization and resampling phenomena.
3. The CNN model misclassified normal data as an incorrect anomaly class when the data had a feature in which a similar shape was shown in the anomaly class.
4. Rule-based classification requires definitions of data anomalies and the selection of the associated threshold values as prior knowledge. This requirement is the most important reason for developing the CNN-based classification, while the deep learning-based approach used to date also has clear drawbacks as discussed in this paper.  
Both the rule-based and CNN-based classifications have advantages and disadvantages with respect to their applicability. The rule-based classification requires prior knowledge necessary to determine threshold values for the data anomalies sought. On other hand the CNN-based classification is free of such definitions of data anomalies; however, information loss due to the conversion of data into images can be an issue. Those characteristics of the rule-based and CNN-based methods discussed in this study result in differences in applicability, depending on target SHM system.  
Conceptualization, S.-H.S.; methodology, I.U.K. and S.-H.S.; software, I.U.K., S.J. and S.-H.S.; validation, I.U.K. and S.J.; investigation, S.J.; writing--original draft, I.U.K.; writing--review and editing, S.J. and S.-H.S.; supervision, S.-H.S.; funding acquisition, S.-H.S. All authors have read and agreed to the published version of the manuscript.  
Figure 21: Misclassification of the _Normal_ class into the (**a**) _Outlier_ and (**b**) _Drift_ classes.  
**Funding:** This research was supported by the Sungkyunkwan University and the BK21 FOUR (Graduate School Innovation) funded by the Ministry of Education (MOE, Korea) and National Research Foundation of Korea (NRF), as well as by a grant (RS-2020-KA156887) from the Smart Construction Technology Development Program funded by the Ministry of Land, Infrastructure, and Transport of the Korean Government.  
**Data Availability Statement:** The data presented in this study is available on request it from the Korea Authority of Land and Infrastructure Safety.  
**Conflicts of Interest:** The authors declare no conflict of interest.