Abstract
This paper describes the procedures for development of signal analysis algorithms using artificial neural networks for Bridge Weigh-in-Motion (B-WIM) systems. Through the analysis procedure, the extraction of information concerning heavy traffic vehicles such as weight, speed, and number of axles from the time domain strain data of the B-WIM system was attempted. As one of the several possible pattern recognition techniques, an Artificial Neural Network (ANN) was employed since it could effectively include dynamic effects and bridge-vehicle interactions. A number of vehicle traveling experiments with sufficient load cases were executed on two different types of bridges, a simply supported pre-stressed concrete girder bridge and a cable-stayed bridge. Different types of WIM systems such as high-speed WIM or low-speed WIM were also utilized during the experiments for cross-checking and to validate the performance of the developed algorithms.
Highlights
The concept of road vehicle weigh-in-motion (WIM) was first introduced in the 1950s in the UnitedStates
The application of artificial neural networks (ANN) to the Bridge Weigh-in-Motion (B-WIM) was attempted in 2003 by Gonzalez et al for noise removal and calibration of the system [11], and in 2005 as a research project conducted by Korea Expressway Corporation
The gross vehicle weight (GVW) is calculated basically using strain readings of main girders and/or cross beams by GVW calculating ANN, axle weights are resulted by multiplying GVW and axle weight distribution factors (AWDFs) which is the output of another ANN
Summary
The purpose of this system was to overcome the drawbacks of static weighing and acquiring traffic information such as weight, speed, passing lane, axle spacing, and type of vehicle without interference with the traffic flow. To achieve these objectives, various sensors are installed beneath pavement layers or on a bridge superstructure and the acquired sensor signals are analyzed and saved. High-speed WIM systems were developed to improve WIM systems, but the development suffered difficulties in attaining acceptable accuracy due to the sensitive dynamic interactions between vehicles and pavement surfaces. In Korea, a high-speed WIM system was developed by the Korea Highway
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