Sounding the alarm

By on 21 August, 2018

A landslide near Cusco, Peru, March 2018. Image via Wikimedia Commons.

An advanced modeling tool developed at the University of Melbourne can predict boundaries of landslides up to a fortnight before they occur.

Researchers at the School of Mathematics and Statistics have built a model that can analyse surface movement at the microstructure level, able to detect the patterns that are the telltale signs of a developing collapse.

One such pattern is infinitesimal motions that change over time, becoming synchronised as the risk of ‘failure’ increases.

Professor Antoinette Tordesillas said there are always warning signs in the lead up to an event, but the difficulty is in predicting where they will occur.

“These warnings can be subtle. Identifying them requires fundamental knowledge of failure at the microstructure level – the movement of individual grains of earth,” she said.

Two weeks prior to the landslide: black pixels are those predicted to be in the landslide boundary, otherwise the pixel is coloured from blue to red to show small to large cumulative surface movements.

“In the beginning, the movement is highly disordered, but as we get closer to the point of failure – the collapse of a sand castle, crack in the pavement or slip in an open pit mine – motion becomes ordered as different locations suddenly move in similar ways,” she said.

“Our model decodes this data on movement and turns it into a network, allowing us to extract the hidden patterns on motion and how they are changing in space and time. The trick is to detect the ordered motions in the network as early as possible, when movements are very subtle,” Professor Tordesillas said.

After the landslide: the location of the boundaries is now obvious.

Tordesillas said that the current effort builds on two key developments: big data analytics, and recent knowledge of the defining patterns of failure from high-resolution measurements in discrete materials, noting that sorting the patterns that matter for early prediction from other patterns present in the datasets is critical to the model’s success.

She said that potential of this technology’s application in future is colossal and is likely to develop quickly, hinting at another milestone on the horizon.

“Imagine if we could collect data on movements of natural and man-made slope surfaces and structures from a small portable device in real time,” she said.

“We can then take that data and return within minutes a probability of a collapse happening in that structure or slope surface. We are getting close — we just need more data to test the codes on.”

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