Scientists at the University of Surrey, UK, have developed a new algorithm to help structural engineers assess the condition of bridges, as well as alert them to any quick repairs.

Most of the organisations use structural health monitoring systems to evaluate the health of bridges, as well as determine the weight of traffic it can support each day.

The process generates a very high sampling rate of data with some reaching at least 10Hz, as well as adds gigabytes worth of information, making it difficult to manage.

In a paper published by the journal Measurement, the scientists at University of Surrey illustrated the process of how they created an algorithm that can compress large data from bridge monitoring systems into manageable sizes.

The scientists utilised a dictionary learning method called K-means Singular Value Decomposition (K-SVD) to compress data from a system that is used to monitor the conditions of Lezíria bridge in Portugal.

“We believe that this approach shows that you can dramatically reduce the large data into a much manageable size without losing information – which is critical to structural engineers.”

The team utilised its algorithm to 45,000 data per channel per hour procured by Bridge Weight-in-Motion system, a monitoring application.

It achieved an approximately lossless reconstruction from the information of less than 0.1%. Other processes also required 50% of the total data to reach similar reconstruction accuracy.

University of Surrey scientist and lead author of the paper Dr Ying Wang said: “Many authorities find it difficult to house the data they have for their bridges and other infrastructure – with hundreds of thousands, sometimes millions of cars using some bridges every day.

“We believe that this approach shows that you can dramatically reduce the large data into a much manageable size without losing information – which is critical to structural engineers.”