Spring thawing occurs when a roads top layer melts while the bottom part is frozen due to soil frost. The frozen layer blocks water from draining and the roads stability is decreased.
Affected roads are then damaged or demolished by, amongst others, heavy lumber trucks. Today the Swedish road administration degrades or closes public roads with decreased stability. This is a financial heavy arrangement especially for the lumber industry, which gets disconnected from their products and thereby their main income.
Today spring frost costs the lumber industry about 510-590 million SEK per year. The main part of these costs is spent on increasing stock sizes. The cost will increase to about 650 million SEK per year due to larger quantities of lumber and an increased use of bio-fuel.
The purpose of this project is to evaluate the need for a system capable of detecting decreased load bearing capacity, both in form of forecasts and in real-time. The use of post season information used during maintenance planning is also evaluated.
- Bearing situation on the gravel road network
- Current bearing situation
- Bearing forecast (Up to 10 days forecast)
- Historic bearing information
The results from the BiFi-project have so far been very successful. The technology to use vehicles to detect the bearing strength of gravel roads has been found very promising.
In part 1 of the BiFi-project an algorithm has been developed based on collected real-time data from a vehicle’s standard sensors. Through data analysis, a method of determining the load bearing capacity of the roads that were driven on with cars was established.
To test the algorithm and model - extensive field trials have been carried out together with reference measurements.
Using the well proven method based on DCP- Dynamic Cone Penetrometer a comprehensive set of reference data was established. This method was also complemented by measurements using a FWD - falling weight deflectometer. A conclusion from this was that the FWD as a method is not very useful during the thawing period since high water content in the road bed gives rise to errors for the FWD.
To ensure the quality from the cars additional sensors were used by reference accelerometers that were fitted to the vehicle in order to give an indication of the quality of the vehicle’s own accelerometer data.
Using the information available within modern cars and data from RWIS – road weather information systems makes it possible to find solutions for detection of different kinds of maintenance needs.