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Implementation of weigh-in-motion (WIM) systems.

Filetype[PDF-3.16 MB]


  • English

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      Final report; 6/14/2000-12/31/2006.
    • Abstract:
      This research finished the development and implementation of a novel and durable, higher voltage,

      and lower temperature dependant weigh-in-motion (WIM) sensor that was begun under an earlier

      research project. These better sensors will require fewer lane closings and replacements than the

      existing sensors. They will also aid the Departments of Transportation to better identify those

      vehicles, which use the nations major highways, that do not comply with the current weight

      restrictions that are placed on larger vehicles. The primary focus of the research was to create a full

      scale WIM sensor that is less temperature dependent and more durable than traditional WIM sensors.

      Traditionally, the data collected from the sensor may be utilized in two ways. The first is by using

      static vehicle effects on the sensor, which corresponds to the weight of the vehicle, this data can be

      used for enforcement of the vehicle legal weight limits. The second is by using the dynamic loading

      of the sensor, which relates to the actual loading that the roadway is experiencing, this data will be

      useful to engineers who must design the roadway as well as plan for repair schedules. However, there

      is a growing trend to broaden the use of WIM data and use the data to its fullest extent. Instead of just

      using WIM data to screen commercial vehicles or for pavement design; there is a new recognition

      that good data can be useful for bridge structural analysis, safety analysis, traffic control and

      operations, freight management and operations, facility planning and programming, and standards

      and policy enforcement as per the recent report “Effective Use of Weigh-in-Motion Data, the

      Netherlands Case Study” FHWA October 2007. In lieu of this development, the need for better

      sensors to provide good data is more important today than ever before.

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