Abstract
In recent years, weigh-in-motion systems based on embedded sensor networks have received a lot of attention. However, how to improve the accuracy of multi-sensor weigh-in-motion (WIM) systems while keeping costs low remains a challenge. In this paper, a numerical simulation method is presented to analyze the relationship between sensor location and the accuracy of static weight estimation. The finite element model of a WIM system is developed, which consists of three parts: a pavement model, a moving load model and two types of sensor models. Analysis of simulation results shows that the ability of sensing dynamic load is closely related to the installation depth of sensors and pavement material. Moreover, the distance between the moving wheel and sensors has a great impact on estimating performance. Gaussian curve fitting could be used to reduce weighing error within a limited range. Our work suggests that much more attention should be paid to the design of the sensor layout of a WIM system.
Highlights
The goal of a weigh-in-motion system (WIM) is to measure the static weights of vehicles travelling at highway speeds [1,2]
Many research efforts have been devoted to accurately estimate static weight based on dynamic load [5]
Finite element analysis is System a numerical method for the approximate solution physics and engineering problems, which is significantly superior to theoretical analysis when of studying engineering problems, which is significantly superior to theoretical analysis when studying problems with nonlinear properties and complex loads
Summary
The goal of a weigh-in-motion system (WIM) is to measure the static weights of vehicles travelling at highway speeds [1,2]. Different from the above work, our study focuses on the relationship between the measured dynamic loads and sensor locations. A numerical simulation method is presented to analyze the effects of sensor location on accuracy of dynamic load estimation. Dynamic simulation of embedded sensors subjected to a moving load provides more accurate insight into the characteristics of responses, which will obviously benefit the design of the sensor layout of a WIM system.
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