# BOPVis: Bridge Monitoring Data Visualization for Operational Performance Mining
## 3 User Study  
We conducted a user study to identify the bridge engineering analysts' pain points. The bridge engineering analysts were invited to the field of bridge structure monitoring for the interview. The user interview outline mainly included three parts: (1) workflow, (2) bridge monitoring data processing and analysis tasks, and (3) user needs and expectations for the BOPVis system. Specifically, the questions include:  
* What is your current workflow?
* What tools or software do you use now?
* What specific functions can this tool or software achieve?
* Bridge monitoring data processing and analysis tasks:
* How do you get the data and what is the data format?
* How is the data processing carried out?
* What is the task of data analysis and how is it accomplished?
* User needs and expectations for the BOPVis system:
* What do you think are the advantages of the current workflow?
* What challenges do you see in the workflow?
* What tasks does the BOPVis system need to help you accomplish?  
In order to obtain the authentic and original working behavior of the bridge engineering analysts, the workflow was obtained by the on-the-spot observation of their working process. After sorting out the workflow, a semi-structured in-depth interview [26; 27] was carried out in this user study. Since the questions are open and subjective, semi-structured interviews mean that the interviewer can adjust the interview content according to the specific situation of the interviewees' answers during the interview.  
We conducted interviews with three experts in bridge monitoring data analysis at CCCC Highway Consultants Co., Ltd., which is headquartered in Beijing, China. By setting open questions to a certain extent, the interviewer can dig deeply into the real thoughts of the interviewees. Combined with the workflow observation and the interviews, we conclude the bridge engineering analysts' pain points presented as a storyboard shown in Figure 1.  
The SHM system collects data from every sensor channel and exports it into binary or text files. To extract operational performance insights from this vast amount of data, the pain points of bridge engineering analysts are summarized below:  
* **Inconvenient sensor localization**. Bridge engineering analysts need to select specific channels for analysis. This process requires repeatedly searching through numerous Excel tables, bridge drawings, and channel instruction documents to locate the relevant data. This is particularly time-consuming and inconvenient, especially for less experienced analysts.
* **Repetitive work in knowledge discovery process**. Once the data to be analyzed are located, analysts plot graphs to observe trends over time and channel correlations. Although there are a limited number of commonly used charts, such as time-series plots and correlation matrix graphs, these must be regenerated frequently due to the hundreds of channels. Creating these static charts in Excel or MATLAB is time-consuming and lacks flexible interaction.
* **Cumbersome data processing.** The graphs generated may not always present useful results, often due to abnormal data. Consequently, analysts must process the data and repeat the knowledge discovery process. However, data processing in Excel is cumbersome and difficult, adding to the challenges faced by bridge engineering analysts.
* **Manual data cleaning**. The original data often contains many obvious abnormal data points caused by sensor damage and other factors. Marking and removing these abnormal points cannot be carried out on the static charts and must be performed manually.
* **Data preprocessing.** Preparing data for analysis requires engineers to write their own code to build models in MATLAB. This process is labor-intensive and adds to the workload. Developing simpler scripts specifically designed for bridge operational performance management could greatly reduce the repetitive effort involved in data preprocessing.