# Modal Strain Energy-Based Structural Damage Detection Using Convolutional Neural Networks
## 4 Discussion and Conclusions  
It can be seen from Section 3.1 that the CNN was effective in detecting single damage and multiple damage. In this paper, the simulation data were used to train the network, and then the experimental data were applied to test the network, which achieved excellent detection results. In practical applications, it is usually difficult to get enough training samples for actual structures. This issue could will hinder the applications of ANNs in structural damage detection (especially for large structures). In this paper, it was overcome because the missing data of measurement were supplemented by simulation data; the CNN test results show that it was effective. The current study shows that CNNs could learn the damage features from finite element models firstly, and, thus, the damage of the real structure may be accurately predicted.  
Compared with the BP neural network, the damage detection result of the CNN was better. The intact structure and damaged structures have different features, and different damage locations also show different features. In this paper, the feature of damaged structures was the MSECR. By comparing the damage detection results of Sections 3.2 and 3.3, it was found that the BP neural network was effective for detecting a single damage location, but it was inaccurate in detecting multi-damage and intact structure. Nevertheless, these features (intact structure, single damage, and multi-damage) can be extracted by the CNN.  
In this paper, the reduction of elastic modulus does not represent all real damage cases in engineering structures. Other damages, e.g., corrosion, cracks, and degradation, may also induce changes in mechanical parameters. Therefore, it is necessary to combine other technologies (e.g., image recognition to detect the surface of structures) for comprehensive damage detection in real structures.  
Based on the above discussion, the following conclusions are drawn:  
1. As a classifier, the CNN can accurately detect structural damage.
2. The CNN was more effective than the BP neural network in damage detection.
3. The experimental verifications proved that the CNN can be trained by simulation data and applied to measured data.
4. It was feasible to supplement the missing data of the experiment with the simulated data in the CNN input.  
**Supplementary Materials:** They are available online at [[http://www.mdpi.com/2076-3417/9/16/3376/s1](http://www.mdpi.com/2076-3417/9/16/3376/s1)]([http://www.mdpi.com/2076-3417/9/16/3376/s1](http://www.mdpi.com/2076-3417/9/16/3376/s1)).  
**Author Contributions:** [PERSON] contributed to the paper in conceptualization, methodology, investigation, formal analysis, original draft preparation, software, visualization, and data curation. [PERSON] contributed to the paper in conceptualization, methodology, investigation, formal analysis, original draft preparation, and supervision. [PERSON] and [PERSON] contributed to the paper in investigation, formal analysis, and review and editing. [PERSON] contributed to the paper in conceptualization, methodology, original draft preparation, and review and editing.  
**Funding:** This research received no external funding.  
**Conflicts of Interest:** The authors declare no conflicts of interest.