## 4 Conclusions  
In this paper, a procedure of anomaly detection in bolted connections based on displacement obtained using DIC during dynamic measurements is presented. The usage of the information on horizontal or vertical displacements enabled the proper detection of failure in up to 80% of the analyzed cases. Enrichment of the data with rotations, however indirectly measured (the direct estimation of rotations in a form of the first derivative of displacement fields was not considered), increases the procedure efficiency: in the detection of a change to 100% and in the determination of the number of loosened rows to 95% and their location to 94%, with the number of false alarms below 1%. In every considered type of ANN, the least recognizable anomalies were S11, S14, and S44. The differences between them were too small to precisely separate these cases. Despite that, the obtained results are considered to be very good, especially taking into account the size of the studied anomalies. Taking into account the influence of the dimensions of the observed area and the camera's resolution, it can be stated that the proposed method is suitable for local applications, as it is in this paper. Although DIC measurement application in in situ examinations still requires more testing, its usage in SHM for civil engineering structures affords new possibilities. DIC is a contactless measurement technique, so it is especially useful for monitoring parts of structures that are hard to reach. There is no necessity to mount signal cables, so the influence of long-length cables on the registered signals has been eliminated.  
Figure 10: **(a)** Optimization of ANN architecture (9-H-1 ANNs for determination of the number of loosened rows), **(b)** classification results using ANNs with architecture 9-30-1, adopted classes: S00: 0; S11, S22, S33, and S44: 1; S12, S14, S23, and S34: 2.  
Figure 9: **(a)** Optimization of ANN architecture (9-H-1 ANNs for determination of the type of damage), **(b)** classification results using ANNs with the architecture 9-30-1; adopted classes: S00: 0, S33: 1, S22: 2, S23: 3, S44: 4, S34: 5, S14: 6, S11: 7, S12: 8.  
The sensors' power supply problem, as in the case of wireless sensors, does not exist. Depending on the optical system applied (e.g., a type of adopted lens), it is possible to observe places that are either far away or very close (and are very small).  
The proposed procedure is not a replacement of existing ones (as, e.g., the simple but very efficient signal-based SHM), but it can be rather an alternative and complement for traditionally applied tools. It has to be taken into account that the procedure does not work in real time, since the DIC calculations have to be done off-line; the observed area should be visible and properly prepared (random pattern). Moreover, the approach discussed in the paper needs rather expensive hardware, since the excitation frequency was quite high compared to those that can be found in real buildings or bridges. In consequence, the use of a high-speed camera was required and very good, with stable lightning conditions for registering pictures with extremely short exposition time; in case the excited/monitored frequency is lower (a few hertz), such sophisticated hardware will not be required and the system will be much cheaper.  
Conceptualization: D.Z. and B.M.; methodology: D.Z. and B.M.; investigation: B.T. and D.Z.; software: D.Z.; writing--original draft preparation: D.Z.; writing--review and editing: B.M. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Polish Ministry of Science and Higher Education grant to maintain research potential.  
The authors declare that there is no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} ANN & Artificial Neural Networks \\ DIC & Digital Image Correlation \\ SR & Success Ratio \\ \end{tabular}