# A Novel Decision Support System for Long-Term Management of Bridge Networks
## 2 Materials and Methods
### Integration of Nondestructive Evaluation Data  
Effective utilization of NDE data is important for the decision support system to generate objective and reliable decision strategies. The quantified information from NDE can be utilized by machine learning models to make very accurate predictions. The goal in integrating NDE data into the bridge management framework is primarily to overlay the local defects on the bridge components and use the converted data in the LSTM model for defect growth prediction. To integrate the NDE data in the system, the data frames are encoded in XML format, which is also compatible with FHWA's Robotics Assisted Bridge Inspection Tool (RABITTM) [25]. As shown in Figure 5, the data frame is composed of header information about the structure and the readings with x and y locations.  
A standardized format for NDE data is important for successful communication between different technologies. However, with many types of NDE equipment, manufacturers, data formats, creating such a standardized format is difficult in the near term. Over time, manufacturers will start supporting a specific export format if a certain bridge management system becomes widely adopted. Visualizing NDE data is also important for inspectors to make better decisions. Therefore, a decision support system should also be capable of reading the point data to overlay damage information on a 3D bridge model. In the implemented web-based platform, NDE inspection data can be retrieved by either uploading exported NDE files from IRT, GPR, Ultrasound, LIDAR, UAV, etc., or via manual entry of visual inspection reports. The visualization module in the software implementation displays the imported NDE data in an inspection timeline, allowing the infrastructure owner to investigate the damage condition at different time steps (see Figure 6).  
For a rational and quantifiable condition assessment, it is very important to use a bridge management plan that can generate remediation strategies for different bridge inspection practices [26]. The proposed decision support system can integrate NDE data and calculate element condition states according to major bridge inspection guides (e.g., AASHTO, FHWA, state DOT inspection guides). However, for most inspection guides, the condition state limits need to be quantified for effective utilization of NDE. After NDE data are successfully imported and condition states are determined, the proposed system calculates an important decision ranking parameter, bridge health index to determine  
Figure 5: Example NDE data collected from a bridge deck (electrical resistivity).  
overall structural health of the bridge. Health index (HI) is calculated for each year using Equation (1). This equation was first introduced in a TRNews article [27]. HI is defined as the ratio of the current element value (CEV) to the total element value (TEV) on the bridge.  
\[\text{HI}=(\sum\text{CEV})(\sum\text{TEV})\times 100 \tag{1}\]  
The implemented web-based tool uses element condition state limits defined by AASHTO Guide Manual for Bridge Element Inspection [28] and calculates the Health index for each inspection year, as shown in Figure 7. In the implemented system, the element inspection data can easily be exported to formats compatible with federally used bridge management software such as AASHTOware BrM [29] and LTBP InfoBridge [30].  
Figure 6: Visualization of NDE data and overlay of damage information.  
Figure 7: Web-based implementation analyzing historical NDE data.