# Bridge Health Monitoring via Displacement Reconstruction-Based NB-IoT Technology
## 1 Introduction
Another time series featured-based approach is introduced by [PERSON] et al. [14], in which the damage index is extracted from vibration signals and processed using the maximum-likelihood method. Besides tracking dynamic features, other researchers [15; 16; 17] constructed a static influence line deflection induced by a moving truck under quasi-static velocity to deliver a reliable indicator. Another notable featured approach is conducted by [PERSON] et al. [18]; motion sensors are attached to a passing vehicle, the Mel-frequency cepstral coefficient is then applied, and a damage indicator could be determined. Either model update-based or feature-based approaches require extraction of substantial and measurable information from bridges. Besides selecting the designated feature among many of them, determining an optimal device is another important issue, in which cost and durability are two main concerns. An accelerometer is the most commonly used device compared to other motion sensors due to its low cost. Another applicable monitoring device is the fiber optic sensor suggested by [PERSON] et al. [19]. In their approach, the strain fiber optic sensors are embedded in the concrete deck during the construction period of the bridge. Global positioning system (GPS) technology is another possible method to extract bridge feature information [20]. In their conclusion, weather and environmental conditions can affect the measuring deflection of a bridge if GPS is used. This study proposes to use a micro electro mechanical system (MEMS) accelerometer with an in-house displacement reconstruction technique for bridge health monitoring. In addition, Levels 1 and 2 damage identification are also provided. The fundamental frequency, which is one of the commonly used features, is proved to be a poor indicator by recent research when vibration is induced by passing vehicles [21]. Thus, this study utilizes recorded acceleration data induced by passing vehicles and transforms these records into dynamic displacements as the feature for our study. Based on [PERSON] et al. [22] and the Newmark-beta method, a new formulation of the reconstructed displacement algorithm is proposed here. The proposed algorithm can deliver more accurate results without sacrificing cost, and details are introduced in the section of Methodology. Continuous records of reconstructed displacements are used to diagnose the bridge conditions. If any significant difference is observed, damage may occur. Since such a process is repeatedly performed at different locations, a platform or network should be built to fulfill the needs: a long record at several places. In light of this, the narrow bandwidth Internet of things (NB-IoT) technology is adopted and implemented with MEMS sensors in our study. With NB-IoT implementation, the recorded data could be transported directly to servers that are usually located at offices and not connected to the sensors. Table 1 provides a summary of the aforementioned approaches. Users can select a suitable method based on their own needs. Detailed comparison of accuracy, noise resistance or sensitivity to different loading conditions is worth investigation; however, it is beyond the scope of the current study. To demonstrate the accuracy and applicability of the proposed method, three different experiments are carried out. (i) Small scale bridge testing with passing vehicles under two different bridge conditions (original and damaged) is considered. In this first experiment, the accuracy of the proposed reconstruction algorithm from accelerations will be validated by comparing the calculated displacements with those from linear variable differential transformers (LVDT). The proposed Level 1 damage detection is also performed and compared to that of using frequency indicator. (ii) A numerical model built by FEM with various artificial damage locations is considered. The proposed approach of Level 2 damage detection and the effect of the number of sensors on a bridge damage detection are examined.