Therefore, dynamic moving loads from vehicle exerting on the bridge can be simply replaced by constant moving loads. Based on previous researches, although many techniques have been proposed for bridge WIM to estimate the weights of vehicles, two different assumptions of vehicle loads on the bridge, which is either constant or time-varying moving loads, are often employed.įor the constant moving loads assumption, the vehicle is assumed to pass the bridge without any vertical body motion. Knowing the physical parameters of the bridge such as span length and flexural rigidity, the system can estimate the weights of passing vehicles from those bridge response data coupled with the configuration and speed information of vehicles which are obtained from another set of sensors. In general, B-WIM system monitors the deflection, strain, or bending moment data of the bridge during the passages of vehicles.
CLASS 4 AXLE WEIGHT DISTRIBUTION DRIVERS
In addition, the system is transparent to the vehicle’s drivers so that the obtained weight information is expected to be unbiased. Beside its cost advantage, the system installation and maintenance do not disturbed the traffic flow. The bridge WIM systems deal with an existing instrumented bridge or culvert from the road network. Therefore new alternative weigh system, namely, bridge weigh-in-motion (B-WIM) is developed.
Moreover, their costs of system installation and maintenance are expensive. They, however, take quite long time to weigh each vehicle. The traditional weigh stations are commonly used to weigh vehicles and impose fines or penalties for exceeding weight limits.
The weights of vehicles govern the design requirements for highway infrastructure such as pavements and bridges. Comparing between them, although the weight estimation method assuming constant magnitudes of axle loads is faster, the method assuming time-varying magnitudes of axle loads can provide axle load histories and exhibits more accurate weight estimations of the vehicle for almost of the considered cases. In general, both methods can provide quite accurate weight estimation of the vehicle.
Based on the obtained results, vehicle speed, surface roughness level and measurement error seem to have stronger effects on the weight estimation accuracy than other parameters. The effects of vehicle speed, vehicle configuration, vehicle weight and bridge surface roughness on the accuracy of the estimated vehicle weights are intensively investigated. The effectiveness in term of the estimation accuracy is evaluated and compared under various parameters of vehicle-bridge system. The appropriate number of bridge elements and sampling frequency are considered. Two weight estimation methods assuming constant magnitudes and time-varying magnitudes of vehicle axle loads are investigated. The measured bending moments of the instrumented bridge under a passage of vehicle are numerically simulated and are used as the input for the vehicle weight estimations. The effectiveness of vehicle weight estimations from bridge weigh-in-motion system is studied.