Hackathon 2019 solutions – Team Yellow (Winners)
Traffic flow and loading on the great belt bridge
This was the winning proposal at the Sund & Bælt Hackathon 2019 co-hosted with Rambøll. A presentation about real-time traffic data collection for enhanced structural analysis.
Bridge structures are exposed to a wide range of loadings stemming from e.g. weather (wind, temperature variations, etc.) and traffic (vehicles and trains). All of the different types of loading will have an impact on the bridge lifespan, and it is therefore of vital importance to know these so that they can be included in the planning of the bridge design and the maintenance work, respectively.
On the great belt bridge a lot of equipment has already been installed to measure the weather related loads. Furthermore, it is the plan to install so-called ‘weight in motion’ (WIM) sensors in 2020 which can record the vehicles weights before entering the bridge. However, the WIM devices do not provide any information of the traffic flow is distributed on the bridge – it only tells the engineer what the total traffic load is.
The present group of engineers – Team yellow – therefore suggest installing a set of cameras on the bridge that can detect the position of each vehicle when passing over the bridge. The principle of this is simple; when a vehicle passes the WIM sensors, a camera registers the vehicle numbers plate, which represents a unique ID. When the vehicle passes the following mounted cameras on the bridge, the ID is used to identify its position as a function of time. This allows the engineers and maintenance workers to map the distribution of the entire traffic loading on the bridge as a function of time.
A full map of the traffic distribution recorded over a long-term period (months or years) will be of great value in the prediction of the bridge lifetime. Today, an assumed loading is used in the initial bridge design. Knowing the actual traffic load will give the engineer the possibility to predict more precisely when and where the bridge is more likely to be exposed to critical conditions. One such case could be the prediction of fatigue, which is characterized by a material (e.g. a bridge cable) that breaks due to many repeated load cycles – to estimate these load cycles accurately, the traffic distribution is needed.
See the ppt presentation here.
Read more about the other solutions here: