# A Novel Decision Support System for Long-Term Management of Bridge Networks
## 3 Results  
The artificial data for initial training were created from the ranking methodology discussed throughout this section. An example set of data was created from Florida's NBI bridges. The data were split into different congressional districts to represent the separate transportation networks. The training data have the following features in columns: structural adequacy, serviceability, bridge importance, bridge value index, and available fund. The values for district available funds were obtained from the _FDOT 2019 Work Program Instructions report, under District Bridge Repair and Rehabilitation Funds_[55]. A small sample from the generated data is shown in Table 1.  
The TF-Ranking model was trained on the generated dataset only for 15,000 steps to initialize the model without causing overfitting. The model will gradually improve in the deployed decision support system and incrementally learn the valuable deep features of the ranking methodology as the infrastructure owner makes changes in the ranking orders. The training performance of the model is shown in Figure 13. Training loss indicates how successfully the model converged with the training batches of the data at each step. The evaluation metric that was used in the model is normalized discounted cumulative gain (NDCG), a commonly used metric to measure ranking quality [56]. Since the ranking data used in the training was generated from an analytically defined mythology, the TF-Ranking model easily fits the data during training, yet will not perform well on real-life ranking without a fine-tuning. As real data arrives, the model will improve incrementally. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**District**} & **Interselected** & \multirow{2}{*}{**Type**} & **Structural** & \multirow{2}{*}{**Servicesability**} & **Bridge** & **Value** & **Available** & **Decision** \\  & **Feature** & & **Adequacy** & & **Importance** & **Index** & **Fund** & **Ranking** \\ \hline \multirow{2}{*}{FL-2} & Brown & \multirow{2}{*}{Prestressed} & \multirow{2}{*}{37\%} & \multirow{2}{*}{20\%} & \multirow{2}{*}{10\%} & \multirow{2}{*}{14.8} & \multirow{2}{*}{$18.3M} & \multirow{2}{*}{40} \\  & Creek & & & & & & & \\ \hline \multirow{2}{*}{FL-4} & Palm & \multirow{2}{*}{Avenue} & \multirow{2}{*}{Prestressed} & \multirow{2}{*}{45\%} & \multirow{2}{*}{25\%} & \multirow{2}{*}{12\%} & \multirow{2}{*}{15.4} & \multirow{2}{*}{$515.9M} & \multirow{2}{*}{42} \\  & Avenue & & & & & & & \\ \hline \multirow{2}{*}{FL-5} & Lake Jesup & \multirow{2}{*}{Prestressed} & \multirow{2}{*}{54\%} & \multirow{2}{*}{29\%} & \multirow{2}{*}{18\%} & \multirow{2}{*}{20.1} & \multirow{2}{*}{$59.2M} & \multirow{2}{*}{38} \\  & & & & & & & & \\ \hline \multirow{2}{*}{FL-1} & Gum Creek & Concrete & \multirow{2}{*}{55\%} & \multirow{2}{*}{30\%} & \multirow{2}{*}{15\%} & \multirow{2}{*}{17.8} & \multirow{2}{*}{$510.3M} & \multirow{2}{*}{45} \\ \hline \multicolumn{10}{c}{...} \\ \end{tabular}
\end{table} 
Table 1: A small sample from the generated data showing bridges from different districts. Figure 12: Multicriteria selections of maintenance actions for bridge decks [54]. The decision selection was implemented inside the decision trees, allowing each deterioration mode to be analyzed inside decision loops. Once the strategy is selected and the funding availability is approved, detailed maintenance actions are automatically generated. An example decision action tree for bridge deck concrete cracking according to maintenance practice of FDOT is given in Figure 14. The maintenance practice is based on the _FDOT Bridge Maintenance and Repair Handbook_[57]. In the decision tree, first, the condition of the cracking is determined, and then the availability of funding is checked through the decision ranking. Until the bridge reaches the target priority, the associated maintenance actions are awaited. Once the funding becomes available (i.e., the bridge falls inside the target ranking), conditional maintenance actions are suggested to the infrastructure owner.