## 6 Conclusions  
This paper investigated the use of a Sparse Auto-Encoder to characterize vibration signals aiming to detect structural anomalies. Features extracted by SAE directly from time-domain accelerations were used as input variables to train a classification model based on the traditional Support Vector Machine. Firstly, the proposed methodology was checked using structural responses from a numerical model of a simply supported beam under different damage levels. Then, in order to verify the SAE/SVM effectiveness in a real-case structure, experimental data from Varzea Nova viaduct in Joao Pessoa (Brazil) were analyzed. Dynamic tests under local traffic were performed on the viaduct before and after a reinforcement procedure.  
For both structures, the proposed approach was able to identify practically all dynamic responses correctly:  
* For the numerical model, the two damage scenarios were almost perfectly detected, indicating that the proposed method is sensitive to the damage level, corroborating its potential for multiple damage and damage quantification problems.
* For the Varzea Nova viaduct tests, the performance was slightly inferior due to the influence of external factors, such as noise, traffic, temperature, among others.  
Figure 14: Várzea Nova viaduct: Confusion matrices—100 SAE internal codes. (**a**) Accelerometer 1. (**b**) Accelerometer 2.  
Figure 13: Várzea Nova viaduct: Confusion matrices—50 SAE internal codes. (**a**) Accelerometer 1. (**b**) Accelerometer 2.  
Furthermore, even though better classification rates were obtained for signals reconstructed with 100 SAE characteristics, the results for dynamic data reconstituted with 50 features were more than reasonable. It is also important to highlight that the SAE features extracted have only 1% or 2% of the total signal's length, for 50 or 100 internal codes, respectively. In addition, the performance of the proposed technique for the accelerometer closest to the damage was superior, which may indicate that the proposed methodology can be better explored in damage location problems.  
Finally, the above considerations encourage the development of computational models using SAE for structural integrity assessment.  
Conceptualization, methodology, validation, formal analysis, investigation, resources, writing--original draft preparation, writing--review and editing, R.P.F., F.d.S.B., A.A.C. and R.L.P.; data curation, R.L.P.; supervision, F.d.S.B. and A.A.C., funding acquisition, F.d.S.B., A.A.C. and R.L.P. All authors have read and agreed to the published version of the manuscript.  
This research was funded by CAPES (Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior, PROCAD \"88881.068530/2014-0\"), CNPq (Conselho Nacional de Desenvolvimento Cientifico e Tecnologico--grants \"304329/2019-3-PQ\" and \"311576/2018-4-PQ\"), FAPEMIG (Fundacao de Amparo a Pesquisa--projects \"PPM-0002-16\", \"PPM-00106-17\" and \"PPM-0001-18\"), UFJF (Universidade Federal de Juiz de Fora) and UFPB (Universidade Federal da Paraiba).  
Not applicable.  
The authors would like to thank the help of [PERSON], [PERSON] and [PERSON] in collecting data from the Varzea Nova viaduct.  
The authors declare no conflict of interest.