# A Novel Decision Support System for Long-Term Management of Bridge Networks
## 3 Results
For instance, minor cracking damage on the deck surface can be repaired using a liquid sealer if there are many cracks. On the other hand, a moderate crack should be repaired with pressure injection if the sealing was not previously made. Figure 14: Example maintenance action tree for concrete bridge deck cracking based on FDOT’s practice. Figure 13: Training results of the TF-Ranking model using the generated dataset. The maintenance strategy generation was also integrated into the web-based system implementation, as shown in Figure 15. The system automatically generates suggested maintenance/repair strategies based on the predefined repair pricing input and the decision tree algorithm used for each deterioration mode. However, it is possible to generate optional strategies based on different decision criteria selection. The system will modify the suggested maintenance actions. The user can also manually change a particular action in the maintenance suggestions and update the cost calculation according to the user-defined unit price list. The maintenance suggestions and the corresponding unit costs were obtained from NCHRP Final Report 668, \"Framework for a National Database System for Maintenance Actions on Highway Bridges\" [58]. In the shown example, the NDE data belonging to high-speed IRT deck scanning were imported to the system, and the suggested maintenance actions for cracks and spalling were automatically generated using the decision trees (it was assumed that this bridge was assumed high priority in the decision ranking). Then, a cost summary is shown to the user indicating the individual cost items for each maintenance action. In this particular NDE inspection, repair for spalling was shown as the major cost item. The enhanced decision support system introduced in this study benefits from novel approaches at multiple levels. When the NDE data are integrated into the system, a powerful deep neural network, CNN-LSTM model predicts the future state of each concrete defect based on the historical NDE input. Another novel approach used in the proposed decision support methodology is that a deep learning-based ranking model, TF-Ranking, prioritizes the bridges for maintenance/repair based on a large variety of factors including bridge assessment history, bridge importance for state traffic, structural condition, serviceability, repair cost, life cycle cost, repair time and funding availability. These factors are grouped under structural adequacy, serviceability, importance, and funding. The adaptive ranking model bases the NBI's bridge sufficiency rating methodology but expands the evaluation metrics to account for the life cycle cost and the future element condition. The system also allows infrastructure owners to calibrate the weight factors for different ranking categories; thereby gives more control over the decision. The TF-Ranking model will quickly adapt the bridge maintenance practice of the infrastructure owner as new training data for fine-tuning are generated automatically from the decision makers' priority adjustments. Figure 15: Maintenance cost analysis module in the software implementation of the decision support system.