How machine learning helped develop a new algorithm that could add life to bridges

April 16, 2018, University of Surrey

A new algorithm developed by the University of Surrey could help structural engineers better monitor the health of bridges and alert them to when they need repair faster.

Many authorities and organisations use structural health monitoring systems to keep track of the health of bridges, along with the weight of the traffic that it withstands on a day-to-day basis. This leads to a very high sampling rate of data, with some reaching at least 10 Hz and databases that have gigabytes worth of on a singular structure - which is expensive to house.

In a paper published by the journal Measurement, scientists detail how they created an algorithm that compresses large data from bridge monitoring systems into more manageable sizes.

The Surrey scientists used a dictionary learning method called K-means Singular Value Decomposition (K-SVD) to compress data from the system that monitors the Lezíria in Portugal. The team applied its algorithm to 45,000 data per channel per hour received by the Bridge Weight-in-Motion - one of the most widely used monitoring applications - and managed to achieve a nearly lossless reconstruction from the information of less than 0.1 per cent. Other methods have shown that they need 50 per cent of the data to achieve similar reconstruction accuracy.

Dr Ying Wang, lead author of the paper from the University of Surrey, 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 ."

Explore further: Picking up bad vibes to gauge bridge health

More information: Helder Sousa et al, Sparse representation approach to data compression for strain-based traffic load monitoring: A comparative study, Measurement (2017). DOI: 10.1016/j.measurement.2017.10.042

Related Stories

Picking up bad vibes to gauge bridge health

May 2, 2007

By monitoring changes in vibrations of bridges it is possible to identify hidden cracks and fractures, according to a Queensland University of Technology researcher.

3-D model could help manage US bridge maintenance crisis

February 11, 2015

Nearly one out of every nine bridges in the United States is deemed structurally deficient and potentially dangerous, according to the Federal Highway Administration. It would cost an estimated $70 billion to catch up with ...

Recommended for you

Electrode shape improves neurostimulation for small targets

April 24, 2018

A cross-like shape helps the electrodes of implantable neurostimulation devices to deliver more charge to specific areas of the nervous system, possibly prolonging device life span, says research published in March in Scientific ...

China auto show highlights industry's electric ambitions

April 22, 2018

The biggest global auto show of the year showcases China's ambitions to become a leader in electric cars and the industry's multibillion-dollar scramble to roll out models that appeal to price-conscious but demanding Chinese ...

Robot designed for faster, safer uranium plant pipe cleanup

April 21, 2018

Ohio crews cleaning up a massive former Cold War-era uranium enrichment plant in Ohio plan this summer to deploy a high-tech helper: an autonomous, radiation-measuring robot that will roll through miles of large overhead ...

How social networking sites may discriminate against women

April 20, 2018

Social media and the sharing economy have created new opportunities by leveraging online networks to build trust and remove marketplace barriers. But a growing body of research suggests that old gender and racial biases persist, ...

Virtually modelling the human brain in a computer

April 19, 2018

Neurons that remain active even after the triggering stimulus has been silenced form the basis of short-term memory. The brain uses rhythmically active neurons to combine larger groups of neurons into functional units. Until ...

0 comments

Please sign in to add a comment. Registration is free, and takes less than a minute. Read more

Click here to reset your password.
Sign in to get notified via email when new comments are made.