# Enhancing Reliability Analysis with Multisource Data:
## 1 Introduction  
Probabilistic approaches (e.g., reliability analysis) have been commonly used for structural design, which requires an evaluation of distributions in loads and resistance. As more Weight-In-Motion (WIM) data become available, probabilistic approaches can aid the decision making process [1] for transportation asset management. For example, bridge live loads (or vehicle weights) have increased over time whereas the capacities of bridge members decrease as they age as a result of natural forces and repeated load cycles. Bridge load rating requires multiple variables including bridge conditions and estimated live loads. Based on a section loss and/or condition rating available in an inspection report, the capacity of a bridge is generally more accurately evaluated than the live loads for load rating. On the other hand, the live loads are considered as one of the most uncertain elements when bridges are being reviewed for load-rating. Bridge owners and decision-makers do not have the information about live loads carried by bridges due to either not having access to the latest WIM data or sheer amount of data involved in analytics.  
There have been numerous reliability-based bridge analyses which assume a distribution of live loads. [PERSON] and [PERSON] [2] and [PERSON] et al. [3] studied a reliability-based bridge assessment. [PERSON] et al. [4, 5] discussed the dynamic effects and how to reliably assess bridge resistance using vibration data. [PERSON] et al. [6] studied the effect of overweight trucks on bridge deck's service life and associated economic impact. [PERSON] et al. [7] and [PERSON] et al. [8] discussed risk and resilience of transportation infrastructure to support planning and design. In a recent study, [PERSON] et al. [9] presented accelerated bridge deterioration due to increasing vehicle weights. In our previous study, [PERSON] al. [10] determined live load factors using WIM data, studied a distribution of live loads, and concluded that there are bridges subjected to significantly higher-than-expected live loads. [PERSON] et al. [11] analyzed bridge condition ratings for the state of Georgia but did not correlate them with live loads. Nonetheless, there are few studies addressing both field-collected live load demands and capacities across an inventory of bridges. The National Bridge Inspection Standards (NBIS) requires load ratings be documented in the National Bridge Inventory [12] database, and they are expected to reflect changes in structural conditions due to repairs, modifications, and/or natural forces and thus measures a bridge's ability to carry live loads based on the latest conditions.  
This study aims to investigate live loads observed in weigh-in-motion (WIM) systems and compare them with the maximum load carrying capacities (or operating rating) provided in the NBI database compiled between 1992 and 2021. While there are several reliability studies conducted using either the NBI data or WIM data, there are few studies that validate a positive relationship between bridge operating load ratings and condition ratings. This step is critically important to synthesize WIM and NBI data, conduct a reliability analysis for a large number of bridges in an inventory, and prioritize bridge monitoring, maintenance, rehabilitation, or replacement (MRR). Thus, the proposed reliability analysis aims to synthesize the past 3-year WIM and respective NBI operating load rating data for a reliability analysis, as well as to identify bridges with high risk, specifically those with a reliability index (RI) below a threshold level (2.0).