WIM-based vehicle load models for urban highway bridge

Abstract

With the rapid development of China's economy, many long-span bridges have been built and put into service. Vehicle load has been changing year by year in terms of the gross vehicle weight (GVW), the wheelbase and the traffic volume, especially the overload of heavy vehicles, which is a major challenge to the safety and durability of bridges. It is necessary to establish the vehicle load model through the measured traffic data for actual traffic conditions of the given bridge. In this study, the weigh-in-motion (WIM) data collected from an operational long-span urban highway bridge located in Wuhan, China, were used to analyze the statistical characteristics of vehicle loads. On the basis of the types of vehicles, (1) Considering the double or multimodal Gauss distribution characteristics of the GVW, the expectation maximization (EM) algorithm was used to estimate the statistical parameters of the Gauss distribution; (2) The generalized extreme value distribution (GEV) was used to develop the statistical model of the vehicle load extreme value; (3) The vehicle load extreme values in the design reference period of the urban highway bridge were estimated by the extreme value type I distribution; (4) According to the axle weight and the axle spacing, a statistical fatigue vehicle load model for the small and medium span urban highway bridges located in Wuhan, China was presented based on the Miner linear accumulated damage hypothesis and the effective fatigue damage principle.

Fatigue load model of orthotropic steel deck for port highway in China

“…Qu et al (2020) used the generalized matrix method to analyze the traffic load of Jinan-Qingdao expressway in Shandong Province, and established a vehicle fatigue model. Li et al (2020) established a fatigue vehicle load statistical model of Wuhan medium and small span urban highway bridge based on axle weights and wheelbases by using the WIM data of a long-span urban highway bridge in Wuhan. Lao et al (2020) evaluate the tonnage of truck freight traffic by combining WIM data and telemetry traffic monitoring (TTM) point flow data, which can be used to evaluate freight performance in statewide areas with low calculation cost.…”

Vehicular Loads Hazard Mapping Through a Bayesian Network in the State of Mexico

“…This data is also required, for example, as input in the design of pavement, new bridges, and in the reliability Miguel Angel Mendoza-Lugo and Oswaldo Morales-Nápoles assessment of existing ones. Several WIM-based traffic load models for bridge analysis have been formulated (see Mendoza-Lugo et al (2019); Li et al (2020); Anitori et al (2017) for example). However, some of the limitations for the wide installation of permanent WIM stations on the road network are the high installation, operation, and maintenance costs.…”