# Bridge Health Monitoring via Displacement Reconstruction-Based NB-IoT Technology
## 1 Introduction  
Over a decade, many researchers have attempted to address the issue of bridge health monitoring, in which one of the main focuses is to deliver a simple and sustainable monitoring procedure. Often, the monitoring procedure is required not only accurately to detect the damaged status but also to minimize the cost, including no interruption in transportation. [PERSON] [1] classify four damage identification levels, in which Level 1 detects damage occurrence; Level 2 localizes bridge damage; Level 3 quantifies the severity of the damage, and Level 4 predicts the remaining service life of a bridge structure. Various approaches are developed to accomplish their own goals of either only Level 1 or up to Level 4 identification. Most approaches could be classified into two groups: model update-based and featured-based approaches. Model update-based methods require users to construct a numerical structural model either using simplified multiple degrees of freedom (MDOF) or a complex finite element method (FEM) model. The numerical model is built using the initial undamaged bridge property, and its dynamicproperty is kept updated based on the extract information from the monitored bridge. Through the identified difference between the updated and original models, one could detect the damage occurrence and location [2; 3; 4; 5]. Dynamic properties such as frequency, mode shape, and damping ratio are often adopted for damage detection. [PERSON] et al. [6] performed a comparative analysis among several damage identification indicators in an actual bridge for identifying damage occurrence and location. However, for a challenging task such as a bridge with insignificant damage status, their methods did not provide consistent outcomes. [PERSON] et al. [6] also indicate that variations of a fundamental frequency and mode shape are poor indicators for damage detection. Other researchers proposed to incorporate the update-based method with an artificial neural network (ANN) for damage identification. In this case, various FEM damage scenarios were trained, and the mode shape was used to determine the damage location and severity level [7]. For example, [PERSON] et al. [8] constructed their ANN using a large set of bridge FEM, and the built ANN is used to predict future accelerations and to calculate the damage indices. In addition to utilizing ANN in the approach of a model update-based method, [PERSON] et al. [9] adopted an optimization technique to enhance the accuracy of the simulated model. In their optimization task, the objective is to minimize the difference between the extracted features from the actual and simulated bridges. The design variables are bridge material properties. It is seen that the accuracy of the model update-based method heavily relies on the selected extracted features from actual/simulated bridges. Besides the extracted features, environmental effects sometimes play an important role and need to be considered. Unlike the model update-based method, the featured-based approach does not require the construction of a numerical bridge model. Similar to the model update-based method, important feature information from the actual bridge is needed. However, instead of incorporating the feature information with the simulated model, the extracted feature information is directly processed to diagnose bridge conditions. One of the most commonly used features is the fundamental frequency [9], which can be obtained through acceleration records or other motion sensors using any available time-frequency transformation tools. Besides the frequency indicator, bridge strain information could be another reasonable indicator [10; 11]. [PERSON] et al. [12] adopted a gravity sensor to measure the tilt angle to differentiate the damaged and original bridges. Another innovative featured-based approach is proposed by [PERSON] et al. [13]; the damage index was calculated through time series analysis combining autoregressive with the exogenous prediction model.