Intelligent platform transforms infrastructure management through predictive modelling and analytics

Intelligent platform transforms infrastructure management through predictive modelling and analytics

RMIT researchers developed a cloud-based software and app that provides data-driven analytics to inform maintenance and management of infrastructure, including buildings, bridges, roads, and drainage systems.

Key points

  • The Central Asset Management System (CAMS) has been developed to support faster, data-driven decision-making about lifecycle asset management tailored specifically to the needs of organisations.
  • The system integrates predictive modelling with real-time field data input via a mobile app, ensuring infrastructure decisions are informed by up-to-date and accurate information.
  • CAMS has demonstrated wide applicability and scalability, supporting more than 4000 facilities across Australia and internationally, including critical infrastructure such as railways and public buildings.
Professor Sujeeva Setunge, Project and Research Lead.
Dr Kanishka Atapattu, Lead Research Fellow

The research problem

Infrastructure assets in Australia are valued at over $3 trillion, yet maintenance has often been reactive and inefficient.

Professor Sujeeva Setunge and her team worked closely with local government and industry, developing the Central Asset Management System (CAMS) to tackle the issue. The cloud-based platform uses predictive modelling to anticipate maintenance needs, reduce costs, help with decision making and improve service delivery.

An app was also developed as part of the system to allow for on-the-spot data collection by buildings inspectors and maintenance staff in the field.

Research background

Community buildings are the second largest asset class managed by local councils and fundamental to service delivery, including accommodation, gyms, community centres, and homeless shelters.

Setunge said the research first begun with a focus on predictive modelling of building deterioration to allow councils to make better decisions about maintenance and repairs.

With funding from the Municipal Association of Victoria and Australian Research Council, she said the team worked with six local councils including the City of Melbourne to develop a solution.

“We first looked at how condition changes could be predicted to give asset managers better understanding of these changes and associated costs,” said Setunge.

“From there they could go from a reactive fix to a proactive, planned and considered approach,” she said.

Setunge said in response to council requests, a CAMS mobile app was also developed by the team and released in 2016 to enable field inspectors to directly enter data into the CAMS while on site.

“We have developed a system that works with the limited information they have on the ground."

“It helps them to capture information precisely so we can improve the accuracy of predictions in future. CAMS also helps our stakeholders to make informed decisions about infrastructure maintenance,” she said.

How does CAMS solve the issue?

The team first created new probabilistic models to predict degradation of community buildings, and then incorporated these into the cloud-based system and app.

RMIT lead Research Fellow, Dr Kanishka Attapatu has helped manage the development of CAMS and said the team’s early stakeholders advised that they needed a system that could cover the entire portfolio of assets.

“The councils also needed to know the associated funding for maintenance, repairs and replacement so that they could prioritise the work and target funding. So, we added in that function,” he said.

Key benefits summary:
  • Proactive Maintenance: Using advanced predictive modelling, CAMS enables predictive maintenance, reducing long-term costs and preventing failures while minimising demolition, rebuilding and associated high volumes of waste.
  • Comprehensive reporting: Simplifying complex analytics to present risk levels, and budget needs, ensuring informed decision-making.
  • Risk Mitigation: Helps identify potential risks early, allowing for timely interventions and enhancing public safety.
  • Sustainable Management: Reduces demolition and rebuilding, optimised intervention, aligning with global trends towards sustainability.

Highlights of the successful research program

Strong stakeholder engagement for success

Setunge said she is particularly proud of the connections made and relationships developed with stakeholders throughout the research program.

I’m proud of the strong engagement we’ve had with the groups that our research is helping.

“We had 10-15 councils come to the early workshops and we could see they were learning from us, and each other", she said

“We were also learning directly from them about what the problem was,” she said.

Other highlights include:
  • The commercial version of CAMS generates enough income to keep the research team who provide user support and create new knowledge such as AI-enabled automated inspections, emissions prediction and disaster resilience.
  • The system has enabled the team to bid for many different opportunities because the platform could already capture an infrastructure system in granular detail. And each detail can have a mathematical function associated with it.
  • The scientific underpinning for all the predictive models has generated trust with prospective clients.
  • The research program is agile and can respond quickly to changes and demands given an in-house team that is also linked to multi-disciplinary research teams at RMIT who can solve complex problems. 

Research impact

Since 2009 when the first iteration of CAMS was developed, it has achieved significant commercial success and been used to manage infrastructure extensively in Australia and overseas, including more than 4000 facilities.

Using CAMS, asset managers can now capture asset condition data and obtain various analysis reports related to asset deterioration, risk and budget forecasting, allowing them to make informed decisions related to maintenance and budget allocations.

International reach

CAMS has been adopted internationally, including by Madrid’s rail authority for predictive maintenance and asset management.

The research team also collaborated with European rail authorities in Milan, Ankara, and Madrid to develop a disaster resilience system capable of predicting the impact and costs of extreme events on rail infrastructure.

Strong commercial partnership

The team has developed a collaborative partnership with building inspection company, MacDonald Consultancy who use the application to inspect the buildings and then also sell the predictive modelling suite. 

Next steps

The system is continuing to evolve with new applications for sustainability and efficiency. Current developments include an energy retrofitting module for buildings, automated inspections through image recognition as part of the CSIRO NextGen Program, and integration of Building Information Modelling (BIM) into infrastructure management.

Key contacts

Professor Sujeeva Setunge
Associate Deputy Vice Chancellor for Research and Innovation.
STEM College

Dr Kanishka Atapattu
Research Fellow
Research and innovation
STEM College

SDGs

sdg11
CAMS predictive maintenance infrastructure asset management sustainability

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