Thursday 27th Jun, 2019

New research on landslides can also assist industry

A software tool developed by University of Melbourne researchers that can predict the boundary of where a landslide will occur can also be applied to stockpiles.

The team, led by Professor Antoinette Tordesillas from the School of Mathematics and Statistics, said there are always warning signs in the lead up to a collapse or ‘failure’, the tricky part is identifying what they are.

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

“Our code for prediction of slope failure is data-driven. It works on data on motion from any bulk solids and particulates, whether bonded or unbonded.”

The code can accommodate additional information such as known triggers of failure like rainfall which is blamed for the recent landslide in Cebu province, The Philippines.

The code combines knowledge of the physics of particulate failure, the patterns they make, and AI (artificial intelligence) techniques.

“Of course, we cannot possibly see the movement of individual grains in a landslide or earthquake that stretches for kilometres, but if we can identify the properties that characterise failure in the small-scale, we can shed light on how failure evolves in time, no matter the size of the area we are observing,” said Professor Tordesillas.

These early clues include patterns of motion that change over time and become synchronised.

“In the beginning, the movement is highly disordered,” said Professor Tordesillas. “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.”

Basically interesting patterns emerge when many grains are clumped or packed together like in a sand pile.

“The grains begin talk to each other through forces,” said Professor Tordesillas.

“It turns out this chatter has a pattern that tells us which clump of grains will end up collapsing, well before the sand pile shows any signs of collapsing.”

The new software focuses on turning algorithms and big data into risk assessment and management actions.

“We’ve examined many types of materials at different scales,” said Professor Tordesillas.

“The tool itself is not aware of where the dataset comes from, it doesn’t matter what you’re applying it to, if it’s a pile of rocks, rice grains, cereal or possibly a concrete wall.

“It’s down to whether you can get data on motion,” said Professor Tordesillas. “If industry is sufficiently worried about failure in terms of bulk materials falling under gravity, then they’ll deploy the necessary resources to measure movement, collect the data”.

Professor Tordesillas said the examples they have studied derived data from radar and are looking for further datasets in order to test the software tool further.