Could robots improve construction waste recycling processes?

New research suggests that robots could usher in a significant transformation in the processing and recycling of construction waste materials.

Researchers trawled through skip bins across Melbourne construction sites, capturing hundreds of photos of materials destined for landfill. These images were then employed to train deep learning (DL) and artificial intelligence (AI) systems to recognise a diverse range of materials and particles found in mixed waste from construction sites.

Led by Monash University PhD candidate Diani Sirimewan, from the Automation and Sustainability in Construction and Intelligent Infrastructure (ASCII) Lab in Civil Engineering, the study lays the groundwork for leveraging advanced robotics and automation in construction waste management. This promising approach stands to replace manual sorting by workers, which poses risks due to handling hazardous and potentially contaminated waste.

The computer-based system demonstrates superior accuracy and efficiency in identifying and categorising recyclable materials compared to human workers. It is also capable of detecting contaminants, which pose risks to both the community and the environment. This capability is particularly pertinent in light of recent incidents, such as the discovery of asbestos-contaminated garden mulch in Sydney parklands.

Despite the recyclability potential of many materials like timber and glass, sorting debris from demolition and construction sites remains a complex task. While significant strides have been made in managing domestic waste, distinguishing between multiple cluttered construction waste items presents unique challenges.

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Sirimewan believes her research is the first to capture detailed images of dense construction, renovation and demolition waste inside bins on construction sites. This breakthrough has enabled her to develop highly advanced recognition and detection models capable of identifying waste obscured by other debris and minuscule contaminants.

Sirimewan is working closely with colleagues who are currently trialling the technology through simulations employing robotic arms. She hopes that this initiative will drive investment in robotics research and development, aiming to improve the efficiency of construction waste processing and recycling in Australia.

“Our deep learning models showed the remarkable ability to recognise the composition of construction and demolition waste streams, including the identification of contaminants,” said Sirimewan. “It’s exciting.”

“The technology could significantly reduce the volume of waste sent to landfills through better-quality recycling – benefiting the environment and reducing the need for workers to be exposed to dangerous and toxic materials.

“We are continually refining our models for application in new robotic technologies and working closely with colleagues who are trialling it through the simulation of robotic arms.”

Sirimewan said Australia urgently needs construction waste recycling plants.

“With the volume of landfill from construction waste expected to balloon, its effective management is a growing problem,” said Sirimewan. “Investment in the entire construction waste management ecosystem supports a circular economy, job creation, manufacturing opportunities and market development opportunities for recycled products.”

Head of the ASCII Lab, associate professor Mehrdad Arashpour, said it is in the national interest to support the innovation of much-needed solutions to the growing waste management problem.

“Every time we demolish or renovate a building or construct something new, a huge amount of waste material is generated,” said Arashpour. “Currently, most of these materials go to waste and end up in landfill, which has significant environmental impacts, not to mention the loss of potentially reusable resources and economic costs.”

The research was published in the Journal of Environmental Management.