## 5 Conclusions  
This paper first presented the basic theory of modal macro strain-based long gauge distributed sensing technology, and deep learning theory. Both longitudinal distribution and circumferential distribution of modal macro strain were investigated and results indicated some characteristics of damage extent, load excitation location and support location. Image-based damage identification using deep learning tools could only identify the external damage of structures while internal damage images could hardly be reflected if images were acquired from outside of the structures. As concluded, the other disadvantages of image-based damage identification is that these images may include the disturbances of noise, and different extents of light and shadows. The advantages of the work presented in this paper overcomes 1D beam-based damage identification; a 3D model was built to reflect the \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Label** & **1** & **2** & **3** & **4** & **5** & **6** & **7** \\ \hline
**Number** & **80** & **80** & **160** & **400** & **20** & **20** & **40** \\ \hline TPR & 75 & 77 & 156 & 388 & 18 & 18 & 37 \\  & (93.75\%) & (96.25\%) & (97.5\%) & (97\%) & (90\%) & (90\%) & (92.5\%) \\ TNR & 704 & 701 & 623 & 379 & 766 & 764 & 748 \\  & (97.78\%) & (97.36) & (97.34\%) & (94.75\%) & (98.21\%) & (97.95\%) & (98.42\%) \\ FPR & 16 & 19 & 17 & 21 & 14 & 16 & 12 \\  & (2.22\%) & (2.64\%) & (2.66\%) & (5.25\%) & (1.79\%) & (2.05\%) & (1.58\%) \\ FNR & 5 & 3 & 4 & 12 & 2 & 2 & 3 \\  & (6.25\%) & (3.75\%) & (2.5\%) & (3\%) & (10\%) & (10\%) & (7.5\%) \\ \hline \hline \end{tabular}  
* Remark: true positive (TP): prediction is positive and the sample is positive. True negative (TN): prediction is negative and the sample is negative. False positive (FP): prediction is positive and the sample is negative. False negative (FN): prediction is negative and the sample is positive.  
\end{table} 
Table 5: Identification testing results. real status of structures. Combined with long gauge distributed sensing technology, modal macro strains were extracted from peak values of power spectral density from the dynamic responses of different locations of long gauges, and a convolutional neural network was applied to circumferential distribution plots of modal macro strain. Compared with direct methods of image extraction for damage identification, the proposed methodologies first study the plot distribution map of structural modal macro strains, which is a sensitive parameter to structural damage. Then by training and testing these \"feature maps\" using convolutional neural network, the extents of damages, external excitation location and support locations can be inherently identified. This is especially suitable for complicated structural types, complex and changing environments. [PERSON], the first author, was responsible for the development of the Deep Learning algorithm introduced in this work and carrying out the bulk of the research project. [PERSON] closely checked and verified all analytical derivations and computational analyses, and suggested the utilization of the procedures that were utilized. He also provided the expertise in neural network and vibrational analysis. [PERSON] provided his expertise and technical background in composite structures and assisted [PERSON] with carrying out the numerical analyses and implementation of Deep Learning algorithm. [PERSON] with his expertise in a wide range of AI-based techniques, development of AI-algorithms for Structural Health Monitoring as well as his expertise in statistical methods worked closely with [PERSON] and [PERSON] throughout the project. [PERSON], as a leading scholar and expert in FRPs and fiber optic sensors, provided valuable guidance and suggestions that greatly helped with conducting this research project. This research was supported by the funds described in the Acknowledgements Section. This work is currently supported by Structural Health Monitoring R&D Project (No. 2017 YFC0805103), and Post-doc funds (No.