# Novel Method for Bridge Structural Full-Field Displacement Monitoring and Damage Identification
## 4 Conclusions  
In this study, a full-field displacement monitoring method based on natural texture features was proposed, and the abnormal characterization mechanism of the displacement vector in damaged sections was investigated. Additionally, a damage identification index was proposed for full-field displacement monitoring of the structure. The main conclusions are as follows:  
1. The image scale-invariant feature transform (SIFT) algorithm can be used to extract the natural texture features of a structure surface. Arranging a fixed point can constrain the positions of the feature points on the structure surface before and after deformation. The FFDV of the structure surface can be extracted by calculating the relative positional relationships between the feature points and the fixed point before and after deformation.
2. A method to calibrate the monitoring resolution of structural images was proposed. The method calibrates the monitoring resolution by using the length of the feature line and the number of pixels represented by the feature line. The calibration results  
Figure 36: Damage identification program.  
showed that the monitoring resolution of the test beam image in this study was 0.1771 mm.
* 3. Results obtained with 3D laser scanning validated the accuracy of the FFDV of the structure. The validation results showed that the maximum absolute error of the full-field vector length was 0.48 mm and that the maximum absolute error of the rotation angle was 0.82\({}^{\circ}\), indicating that the extracted displacement vector can accurately reflect the full-field displacement characteristics of a structure.
* 4. The displacement vector of a structurally damaged section has been shown to indicate an abnormal rotation angle. Based on this finding, a damage identification index was proposed considering changes in rotation-angle rate. The index was proven to be applicable in the full-field displacement monitoring of the structure. Furthermore, the identification effect of this index was validated via various tests. The validation results showed that the identification index indicated an abnormal peak response at the damage location, validating the accuracy of the index in damage location identification.
* 5. The structural FFDV extraction method expands conventional structural deformation monitoring data. Improving monitoring data dimensions renders the damage signal more intuitive, which is beneficial in solving the damage identification challenges caused by sparse monitoring data.
* 6. The full-field displacement monitoring method proposed in this work demonstrated favorable results in the laboratory. However, actual bridge structural environments are more complicated than the laboratory environment. In particular, the noise in images is more significant. Therefore, the noise-interference problem associated with this method is recommended as a subject for future studies.  
X.D. Conceptualization, Methodology, Investigation, Writing--Original Draft Preparation, Data Curation. X.C. Conceptualization, Supervision, Project Administration, Funding Acquisition. W.Z.: Investigation, Data Curation, Validation, Software. Z.Z.: Conceptualization, Methodology, Investigation. R.L.: Writing--Review and Editing. J.M.: Writing--Review and Editing. All authors have read and agreed to the published version of the manuscript.  
This work has been supported by the National Natural Science Foundation of China (grant numbers 51778094, 51708068, and 52208182); Natural Science Foundation of Shenzhen (grant number JCYJ20205311010020).  
Not applicable.  
Data Availability Statement: Data will be made available on request.  
The authors would like to thank the department of the State Key Laboratory of Mountain Bridge and Tunnel Engineering at Chongqing Jiaotong University for their help in this investigation.  
The authors declare no conflict of interest.