# Bridge Health Monitoring via Displacement Reconstruction-Based NB-IoT Technology
## 4 Conclusions  
This study introduces a novel approach for bridge health monitoring utilizing a displacement reconstruction algorithm. The algorithm is developed utilizing the Newmark-beta method, the Moore-Penrose pseudo inverse and the sliding window technique. Level 1 and Level 2 damage detections are considered in this study. Three experiments are carried out to validate and demonstrate the proposed method. Several brief conclusions and discussions could be drawn as described below:  
1. The proposed displacement reconstruction algorithm requires less computational time and has higher accuracy compared to the previous study, as provided in Section 2.1. The input parameters of the proposed algorithm are recorded accelerations, fundamental frequency and window size;
2. Level 1 damage detection is performed by first using FFT to identify the fundamental frequency of a bridge. It is shown that various vehicle weights and velocities could affect the dominant frequency. Window-slide FFT is also performed to examine the variation of the dominant frequency per window, and it shows that dominant frequency varies as different vehicle position and speed;  
Figure 31: Level 1 damage indicator of Wenzhou bridge in September.  
Figure 30: Level 1 damage indicator of Wenzhou bridge in August.  
Although the stiffness difference between the original and the stiffened bridge in the small-scale bridge experiment is slight, the proposed Level 1 damage detection using reconstructed displacement is able to distinguish the difference between the two;
4. The proposed Level 2 damage detection is successfully to predict the damage location for either case: single or double damages for the numerical examples;
5. To apply the proposed damage detection approach to an actual bridge, MEMS accelerometer and NB-IoT are integrated with the proposed methodology. On-site monitoring was conducted from April until November. Based on the proposed Level 1 damage detection, the monitored bridge shows no indication of degradation since April. The proposed method enables on-site monitoring to be conducted without interrupting traffic activity at an acceptable cost.  
Please note that the input of the proposed reconstructed displacement algorithm is the measured accelerations. That is, the proposed method cannot consider the effect of each individual factor, including environmental factors, separately. However, the effects of the environmental factors are included in the measured data, and the corresponding influences are considered. In addition, in a real case, the deformation of a bridge is in 3 directions. Although most failure modes are related to the vertical deflection, some failure modes, such as shear failure, is not, and the proposed method may not be suitable in such case. The proposed method is only suitable for a bridge with failure mode related to the gravity direction. The current approach could only provide an early warning for a bridge system. A mechanical-based model is needed to further clarify the possible failure mechanisms for a bridge.  
Conceptualization, K.-W.L. and J.T.; methodology, K.-W.L. and J.T.; software, K.-W.L. and J.T.; validation, K.-W.L., C.-C.T. and C.-M.L.; formal analysis, K.-W.L. and J.T.; investigation, K.-W.L., C.-C.T. and C.-M.L.; resources, C.-C.T. and C.-M.L.; data curation, C.-C.T. and C.-M.L.; writing--original draft preparation, K.-W.L. and J.T.; writing--review and editing, K.-W.L. and J.T.; visualization, J.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Ministry of Science and Technology of Taiwan under grant number 109-2622-E-011-015-CC2 and by the Public Works Department, Taipei City Government. The supports are gratefully acknowledged.  
The authors are very grateful to [PERSON], [PERSON] and [PERSON] of Kyoto University (Japan) for arranging the downscale testing.  
The authors declare no conflict of interest.