Estimation of Extreme Load Effects on Long-Span Bridges Using Traffic Image Data

Abstract

This paper proposes an algorithm for the estimation of extreme intensity of traffic load on long-span bridges. Most Weigh-in-Motion technologies do not operate in congested conditions which are the governing cases for these bridges. In the absence of Weigh-in-Motion data on the bridge itself, a correlation between vehicle weights and their lengths is established here using a (freeflowing) Weigh-in-Motion database. Photographic images of congested traffic are modelled here for three bridges using weights estimated from lengths and one year of Weigh-in-Motion data. The actual weights are taken from the Weigh-in-Motion data, and the results are compared to test the method. The gaps between vehicles are firstly set to a constant value and later to Beta-distributed values according to vehicle type. The intensity of traffic load for all pictures is calculated and compared to the loads obtained from the recorded weights. A return period of 75-year is chosen to evaluate the extreme values of intensity. The probability that intensity of load is being exceeded is obtained using normal probability paper for both recorded and simulated weights. This study demonstrates the feasibility of the proposed concept of using lengths to estimate the extreme traffic load events with acceptable accuracy.

Identifying Critical Clusters of Traffic-Loading Events in Recurrent Congested Conditions on a Long-Span Road Bridge

“…This pre-September 2017 data is investigated in this paper. The data analysis methods are summarized in this section and explained in detail in [36,37].…”

“…Having established the CDF, Monte Carlo sampling is used to infer a 'typical' weight for the vehicle recorded in the image. While this process is clearly inaccurate for individual vehicles, it has been shown to be quite accurate in probabilistic calculations of the type considered here [36]. Carlo sampling is used to infer a 'typical' weight for the vehicle recorded in the image.…”

“…A microsimulation model is calibrated by OBrien et al [14] based on traffic composition and driver behaviour for different traffic conditions observed in video data. Micu, et al [36] explore the potential of cameras and image processing techniques to collect congested traffic data. The feasibility is established of extracting valuable information from images such as traffic composition, vehicle lengths and bridge load effects.…”