Friday, 10 March 2017

Data-mining and future prediction of railway tunnel behaviours

Hi fellows, 

Here we are again. 
I hope this post finds you well!! Are you ready for the spring? =) 

In this post, I am going to explain why I worked on the monitoring of the health state of a tunnel during my secondment, which has been carried out from September to December 2016 at AECOM. Firstly, the secondment is important during the Marie Curie programme, as it gives the possibility to each Marie Curie fellow to experience new work activities in different frameworks (industries, new academics groups, etc.). 
Particularly, the goals of my secondment were defined with the aim of applying the mathematical methods that I have developed at the university, into the real daily world. 

Mathematical methods? Yes, guys, the aim of my PhD is the development of mathematical methods, which are able to automatically monitor the health state of railway bridges by analyzing the data provided by a measuring system (that is sensors) installed on the bridge!! Did you remember? 

However, during the first month of the secondment, the company was monitoring in real time the health state of a railway tunnel due to the fact that the tunnel was requiring some works. Consequently, it was an ideal situation to try my mathematical methods in a real-case study by analyzing and monitoring the tunnel behaviors! 
However, before working on it, I had to convince my bosses by asking to the project-coordinator of the Marie-Curie scholarship the authorization to switching topic of the secondment... and fortunately, during the last week of October, I get the green light!! (Thank you Mr. project-coordinator)

Anyway, AECOM has monitored in real time a railway tunnel (for example, see the figure below) by using a measurement system made by more than 300 sensors for more than 4 months, as the monitoring process started in August. Each sensor provided a value of the tunnel behavior, for example displacement of the tunnel walls, or strain, etc., every second basically, 24/7. Therefore, you can easily understand that the first problem was the data analysis of such big database.


Example of railway tunnel (property of Community Rail Lancashire)

I would like to give you as many information as possible regarding the method that we applied in order to identify the typical behavior of the tunnel and, more important, to point out the unexpected tunnel behavior, but as we are drafting research articles on it, I cannot. I am sorry. 
I can say that we (TRUSS people) developed and applied a data-mining algorithm, followed by a machine-learning method that is able to predict the behavior of the tunnel in the future, and, as this was pretty good luckily, AECOM asked us for the copyright of the codes in order to embed them into their analysis methods. Not to bad, isn't?

Finally, yes, I know what you are thinking, and I agree, 100%. However, you have to seek your fortune sometimes... =) 

See you soon!! 





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