# Enhancing Reliability Analysis with Multisource Data:
## 4 Discussion  
RIs for bridges in the vicinity of Atlanta and Savannah are also determined. Figure 10 presents a map showing Atlanta- and Savannah-area bridges as well as the adjacent WIM sites. The highway bridges shown in Figure 10a,b are paired with the WIM sites in the vicinity of Atlanta and Savannah, respectively. Some of the bridges in the Savannah area appear to be subjected to very high live loads, which are expected due to container terminals and hence are associated with bridges with a low RI. Both areas, Atlanta and Savannah, need further investigation into traffic logistics, such as the transportation of commercial goods and other factors, including the locations of distribution centers supporting e-commerce, in order to better assess the reliability of the bridges.  
One of the bridges with a relatively low reliability index (see Figure 11) is further investigated in this paper. The bridge's operating LR has been significantly reduced in the NBI. The inspection report also indicates that there is slight beam movement onto caps under \"heavier truck traffic-beams.\" A beam-end crack is also observed. Additionally, erosion is occurring around piles. The study team has investigated other bridges with a relatively low RI and found similar deficiencies which support a decrease in CR and associated LR. However, this was not consistent across all bridges with a low reliability index. In some cases, live loads drive low (<2.0) reliability indices. In reviewing Georgia's bridge inventory, the two ratings (CR and LR) are correlated in a nonlinear fashion. A gradient boosting algorithm and its intricies involving multiple decision trees and neural networks are not a process that one can easily visualize. Thus, it is described as a \"nonlinear\" relationship herein. Artificial neural network models have hundreds of parameters and internal complexity because many transformations are used during training.  
WIM sensors not only provide the weight information but provide information about motor vehicle demographics, which are used for a variety of decision-making purposes in planning and state policy making. Regardless, since the WIM sensors are already installed and maintained, the cost of installation/maintenance is considered as a sunk-cost. The cost of conducting the proposed reliability analysis is relatively minimal (<0.3 M). However, the proposed multisource data reliability analysis process is estimated to save $200 M (=$0.1 M/bridge \(\times\) approx. 2000 bridges) from being able to better maintain physical assets alone. The economic and social benefits from providing more reliable infrastructure is significantly higher than the cost of conducting a reliability analysis (<$0.3 M). The benefits, including associated long-term returns, outweigh the project cost with a benefit-cost ratio of approximately 666 (=200 M/0.3 M), not including economic/social benefits.  
Overall, there are significant benefits in predicting bridge load ratings in terms of condition ratings and comparing them with live loads observed in WIM sensors. Transportation agencies must load rate bridges, but the process is time-consuming and can be subject to errors. Thus, the gradient boosting model can help save time and avoid potential hazards. In addition to risk management and bridge health monitoring, the proposed reliability analysis employing WIM data is expected to enhance failure statistics and associated design process.  
Figure 10: Reliability indices of bridges in the (**a**) Atlanta and (**b**) Savanah areas.