## 5 Conclusions  
In the study, an automatic settlement and crack monitoring model was proposed to automatically obtain the settlement conditions of engineering facilities along the subway in a large area. After analyzing the settlement, UVA is employed to inspect the appearance of the target engineering facilities and collect crack data. The CNN model was used to recognize the appearance inspection data of the target facilities, which can effectively identify the crack information of the target facilities in the complex environment. Finally, the selected crack images are inputted into the quantitative model to obtain the length, width and area information of the cracks. In the study, we developed a settlement monitoring model with data from Shenzhen Metro Line 9 and Qianhai section of Metro Line 5 and verified its accuracy. In the study case, 11 dangerous and abnormal facilities were precisely identified, which verified the accuracy of the model. As for the crack recognition model, a dataset including a variety of materials was first built, containing a total of 8206 photos, which was divided into training, validation, and test sets according to the ratio of 8:1:1. Meanwhile, a VGG16 network architecture consisting of 13 convolutional layers and 3 fully connected layers was built. Through diversified data sources and scientific training methods, the versatility of the crack recognition model in different scenarios was improved, and multi-material crack monitoring in engineering applications was realized. The accuracy of the final model is as high as 98.48%, which is much higher than the accuracy of the human eye to distinguish cracks. A settlement and crack monitoring model integrating PS-InSAR, convolutional neural networks and digital image processing methods for engineering facilities along the subway is proposed in the paper. The model realizes the automated monitoring along the subway and identifying and quantifying cracks in multiple materials in complex scenarios, which improves the monitoring efficiency, effectively reduces the cost of monitoring and site investigation, and protects the safety of practitioners. Meanwhile, the monitoring results \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{5}{c}{**Number of Connected Domains: 38**} \\ \hline \multicolumn{5}{c}{**Total Number of Cracks Detected: 38**} \\ \hline
**Crack Number** & **Position** & **Area** & **Length** & **Width** \\ \hline  
& 1831.00 & 213.00 & 8.60 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The results of the crack analysis. also provide more useful references for expert decision-making. However, there are still some limitations in the settlement and crack monitoring model. Firstly, the functions of this model are to provide references for experts to make decisions, in which only the quantification of settlement cracks has been realized so far. Secondly, although partial automation of crack identification and quantification has been realized, there is no automation of drone control. Finally, the visualization can be further enhanced to help better understand the results of the inspection. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app131911002/s1](https://www.mdpi.com/article/10.3390/app131911002/s1)]([https://www.mdpi.com/article/10.3390/app131911002/s1](https://www.mdpi.com/article/10.3390/app131911002/s1)), Crack Recognition Model; Crack Measurement Model. Conceptualization, Z.D. and H.W. ; Methodology, Z.D., L.L. and H.W. ; Investigation, L.L., Y.L. and W.Z. ; Writing--original draft, L.L. and H.W. ; Writing--review and editing, Z.D., X.W. and H.W. ; Visualization, L.L., X.W., Y.L. and W.Z. ; Supervision, Z.D. and H.W. All authors have read and agreed to the published version of the manuscript. This research was supported by the National Nature Science Foundation of China (Grant No.71974132), the Natural Science Foundation of Guangdong Province, China (Grant No.