# BOPVis: Bridge Monitoring Data Visualization for Operational Performance Mining
## 4 Bridge Monitoring Data Visualization System  
Based on the bridge engineering analysts' pain points obtained by the above user study, we developed a bridge monitoring data visualization system. The technical framework of the system is shown in Figure 2. The system inputs include the sensor location information and the data collected from each channel. The data cleaning and preprocessing are performed on the server side, encompassing tasks such as reading, sorting, identifying abnormalities, validating data, and selecting relevant data. On the client side, users can select monitoring data channels and observe various types of visualization charts for knowledge mining. These visualization charts are used to discover abnormal points, present time-series data, show correlations between different channels, etc.  
Figure 1: The bridge engineering analysts’ pain points.  
In this bridge monitoring data visualization system, the bridge engineering analysts can mine the knowledge of the operational performance by flexible user interaction. The interface of the BOPVis system, shown in Figure 3, includes three functional areas: selection of monitoring spots, monitoring data details, and the correlation panel.  
**Selection of monitoring data channels.** Users can select monitoring data channels directly from the 3D bridge digital model, eliminating the need to search through Excel tables, bridge drawings, and channel instruction documents. The top left panel displays sensors with channel information on a 3D bridge model to facilitate sensor localization. Users can view the bridge from various angles by moving the mouse and zooming in and out by scrolling. In the upper left corner of the 3D model panel, users can select a specific time. Clicking a monitoring point on the bridge pops up a detail panel displaying the position, type of sensor, and detailed data at that moment.  
**Monitoring data details.** The bottom left panel shows monitoring data details in time-series plots, including four types of data: GPS, ambient temperature (TMP), relative  
Figure 3: The interface of the BOPVis system showing three functional areas: selection of monitoring spots, monitoring data details, and the correlation panel.  
Figure 2: The technical framework of the bridge monitoring data visualization system.  
humidity (RH), and wind speeds (WS). Users can switch data types by clicking the corresponding TAB button and choose time intervals using the time bar at the bottom of the chart. A plus sign in the upper left corner of the panel allows users to enlarge it to the whole page for a clearer view. Selecting curves in the time-series plot automatically highlights the corresponding measurement points in the bridge digital model.  
**Correlation**. The correlation panel includes a correlation matrix graph and a parallel coordinate plot to present channel correlations. Users can enlarge this panel for further interactions by clicking the plus sign in the upper left corner.  
**Data cleaning on the server.** Obvious abnormal data points, which differ significantly from normal data, are identified through data observation. These can be classified as abnormal using the k-means clustering algorithm and set to null values. The empty values are then filled using linear interpolation of adjacent non-missing values.  
**Data preprocessing on the server.** Users need to observe trends and correlations of bridge data over long periods, such as a quarter, a year, or more. The data collected from sensors is extensive; for instance, temperature data from one location can reach 36,000 points in one hour. To avoid redundancy, the system averages the daily data when users look at yearly data. It also provides scripts for users to process data at different granularities. Users analyze monitoring data through automatically generated graphs in the monitoring data details and correlation panels. These easy-to-use scripts provided by BOPVis help process raw data on the server side, significantly reducing manual effort and improving efficiency.