Effectiveness of Vehicle Weight Estimation from Bridge Weigh-in-Motion

Vehicle Signal Analysis Using Artificial Neural Networks for a Bridge Weigh-in-Motion System

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 ...

A Wavelet-based Bridge Weigh-in-motion System

Bridge Weigh-in-Motion systems can be used to estimate vehicle parameters such as velocity, axle loads, or the distance between axles without affecting the traffic. These parameters and the classification of vehicles are necessary to determine vehicle overload and hence may help to reduce risks for road users and road infrastructure and allow optimized planning of maintenance work. The Bridge W...

Bridge Damage Detection Using Weigh - In - Motion Technology

Monitoring of changes in bridge response using Weigh-In-Motion systems

Applying Weigh-in-motion traffic data to reliability based assessment of bridge structures

The knowledge of the process of the deterioration of structural resistance is crucial for existing bridge assessment. However, precisely modeling the process of loading also affects the evaluation. Indeed traffic loading is one of the main loads that a bridge is subjected to in its service time. This paper investigates the role of traffic loading on the time-dependent reliability of a given bri...