# Enhancing Reliability Analysis with Multisource Data:
## 5 Summary of the Results and Associated Analysis  
The WIM/NBI data-informed reliability analysis provides a dependable and beneficial approach for monitoring bridges. Bridges with a low reliability index are relatively older, and newer bridges with a low reliability index are exposed to larger-than-expected live loads. To close the information gap and mitigate the adverse selection problem in bridge MRR, the reliability indices (RI) are evaluated with a limit state using vehicle weights retrieved from WIM load sensors as load demands and the NBI's maximum load carrying capacity as resistance. Figure 4 shows the bridge locations as well as WIM sensor locations. Figure 7 illustrates the results of the reliability analysis. It is observed that most of the bridges in Georgia have an acceptable range of RI (>2.0). However, some bridges have a relatively lower reliability index, which requires a careful review of vehicle weights and/or load ratings. Furthermore, the vehicle weights have been rapidly increasing over the recent years. Thus, it is more effective and salient to monitor bridges more frequently on critical routes using the WIM data as it provides the latest vehicle weight information at a WIM collection location closest to bridges. It is also observed from Figure 4 that four sites (sites 2450214 and 2170218 on I-20, sites 217334 and 1750247 on I-16) with older bridges have a comparatively low RI. On the other hand, fewer bridges which are replaced or reconstructed recently (shown as stars in Figure 4) have a reliability index under 2.0 (bridges on I-75 routes). Oddly, site 1270312 on route I-95 shows a very high number of newer bridges with a low reliability index. This may indicate potential errors such as equipment malfunction in collecting the vehicle weight data and/or vehicle imports at Brunswick, a port in the south of Savannah, may be affecting the north bound route (I-95) towards Atlanta. The relationship between the changes in the 'Substructure CR' and changes in Operating load rating indicates that the LR is affected by multiple factors including the substructure condition ratings and age. A XGBoost machine learning model shows a high R-squared value of 0.77 and a low RMSE value (8.16). The regression analysis indicates that nonlinear machine learning algorithms can accurately predict the operating rating and support the assumption that the maximum load carrying capacities (in the Georgia's inventory) in the NBI well reflect ultimate bridge capacities in conjunction with the variables available in the NBI. However, such relationship must be verified for other states' inventory. Figure 7 shows the output of reliability analysis expressed using the percentage of bridges analyzed with live loads obtained from a selected WIM site. Many bridges in Georgia have a reliability index greater than 5.0. In this study, bridges with a reliability index ranging from 2.0 to 5.0 are considered to be within an acceptable safety margin and thus less prone to failure. On the other hand, bridges with a reliability index less than 2.0 are considered possessing a greater risk to the aging bridges in Georgia. Coincidentally, WIM sites with a lower percentage of bridges with a low RI are associated with older bridges (e.g., see Site 2450214 on the I-20 route in Figure 7). Therefore, in such a situation, the stakeholders or the decision-makers may selectively approach the bridges for monitoring and/or MRR decisions. Nonetheless, the WIM/NBI data-driven reliability assessment of bridges should be the new normal in bridge maintenance and health monitoring. The advantage of the  
Figure 11: Selected bridge with a low RI: (**a**) side view and (**b**) substructure view in 2021.  
proposed data-driven bridge risk assessment is that it increases the transparency about bridge safety between owners and users by providing symmetric information. It also provides information about the influence of growing heavy-weight traffic on highways and bulk freight between port and distribution centers/intermodal rail facilities.