DescriptionOverweight (OW) vehicles are known to have a direct impact on the deterioration of the infrastructure systems (Nassif et al., 2015), negatively impacting the service life of bridges. The serviceability and longevity of a bridge are controlled by the service limit states (SLSs). The SLSs for the HL-93 design vehicles have been calibrated in NCHRP 12-83 project (Wassef et al., 2014). However, for the permit vehicles which are increasing in frequency and weight magnitude, the SLSs have not been calibrated through similar procedures. Since bridges need to carry the permit vehicles, it is necessary to assess the safety levels for the current permit vehicles and their corresponding live load factors.Weigh-in-motion (WIM) sensors measure the truck’s axle configuration and weights, making it the primary source for developing design live load models. Although WIM can detect OW vehicles, it is difficult to differentiate between “permitted” and “illegal” OW vehicles by only interpreting the WIM data. This study incorporates the permit application database since 2011 in New Jersey and presents an approach based on the supervised-learning method to identify OW vehicles that are likely to be permit vehicles. The model validity is demonstrated by comparing data from 59 WIM sites to seven years of permit records issued. There is a significant amount of illegal overloaded vehicles that could potentially pose issues to bridges at service. Furthermore, using the proposed approach, the live load statistics of the New Jersey permit vehicle are derived to assist in applying a reliability-based analysis. Results show that, in the absence of actual permit data, this approach is more reliable in identifying or screening the permit OW vehicles for performing live load analysis. The approach is then applied to 10 WIM sites to find the multiple presence events for the permit vehicles and their load effects.
Reliability-based calibration is performed to obtain appropriate live load factors for the Service II and Service III limit states for steel and prestressed concrete bridges, respectively. The statistical parameters of the resistance model are also determined based on existing bridge databases with various geometries and parameters. This study presents the assessment results and the calibration of the target service limit states in both design and evaluation. The assessment computes the target reliability index of the bridges based on the design vehicles and then calibrates the permit vehicles to reach the same reliability index. Results show that the current permit live load factor at the service limit state does not truly reflect the level of safety of girder bridges. The new target reliability indices are determined, and adjusted load factors are proposed accordingly.