## 4 Conclusions  
This study investigated artificial intelligence-based structural health monitoring. Through testing and practical application in a steel truss bridge, it achieved potential performance, in terms of structural monitoring. Along with the detailed explanation in this paper, some main conclusions can be derived:  
* By combining the vibration measurement results of the structure with the artificial neural network, routine structural health monitoring tasks could be reduced. Specifically, an artificial neural network trained using the vibration measurement results were able to localise and quantify preliminary damage, minimising logistics. In this study, the test was applied to a large steel truss bridge in Vietnam (Chuong Duong Bridge). The results show the potential of the method in similar bridges. * The results of the comprehensive vibration measurement of the structure were used for the first time to update the structure; as such, this took time and effort, but this requirement could be reduced many times over in future checks. * An initial FE model was created and updated based on the modal characteristics extracted from the field vibration measurement experiment. Using the PSO algorithm, an FE model with a behaviour close to reality was built. Before the update, the FE model had a big difference from the actual measurement (the biggest difference in frequency was 5.58%, the MAC value was only 0.87). After updating, the similarity between the FE model and the experiment increased significantly (the biggest difference in frequency being 0.74%, MAC value increasing to the lowest value of 0.94). * Based on the updated model, damage scenarios were performed, and data was extracted for input into the ANN. In this study, damages were performed on the model by reducing the stiffness of the main elements. * Creating and organising data from a finite element model is very important if one wants to get good results when training ANNs. With a large number of samples, training the network takes time, but the improved effects after training can make up for this. * Compared with other methods, this approach had various advantages: saving human resources; being able to identify damage in hard-to-detect locations; and reducing the number of measuring points in the case of vibration tests. * In the case study of this research, with a single damage, the ANN was able to identify and quantify the damage relatively accurately. For damage occurring on two elements, since there is no actual data, the network usage after training was measured on the model. The results were quite satisfactory. The case using the data of two simultaneous damages seemed to be more accurately predicted using the network. This can be explained because actual experimental data will more or less have noise, in addition to being simultaneously affected by many factors. Meanwhile, the data used to confirm the case of two damages at the same time was taken directly from the model. * In future studies, the authors will implement a number of different types of structures, such as cable-stayed bridges, suspension bridges, and continuous bridges. At the same time, the current training of artificial neural networks is also quite time consuming, with large data sets. Further studies will also focus on solving this problem by applying algorithms combined with ANN. Conceptualisation, M.Q.T., J.M., H.X.N., T.V.N. and H.S.S. ; methodology, M.Q.T., J.M., T.V.N. and H.X.N. ; software, M.Q.T., B.D.N. and S.N.D. ; validation, J.M. and S.N.D. ; formal analysis, Q.T.N. ; investigation, E.B., M.Q.T. and S.N.D. ; resources, E.B. ; data curation, S.N.D. ; writing--original draft preparation, M.Q.T. and S.N.D. ; writing--review and editing, M.Q.T., J.M. and S.N.D. ; visualisation, B.D.N. and M.Q.T. ; supervision, J.M., H.S.S., H.X.N. and T.V.N. ; project administration, J.M. and S.N.D. ; funding acquisition, J.M., E.B. and S.N.D. All authors have read and agreed to the published version of the manuscript.