About CAMS

CAMS is an outcome of research conducted by RMIT University in partnership with more than 15 local councils in Victoria, Municipal Association of Victoria, Melbourne Water and VicRoads. Integrate Australia Pty Ltd. has been a partner in the first version of CAMS for buildings. CAMS underpins award-winning research on deterioration forecasting and optimised decision making.

Infrastructure assets in Australia represent a vast investment built up over many generations and are valued at approximately 100 billion dollars. Sustainable management and new design of public infrastructure such as buildings, drainage, bridges and roads for long term performance require a good understanding of the deterioration process, which is a function of a multitude of parameters. CAMS supports a data driven methodology for decision making related to life cycle management of infrastructure. Using CAMS , asset managers can 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.

System & Asset Component Register

Consistent division of a system in to elements – 9 level collapsible hierarchy for easy navigation and data gathering. GIS integrated.

Forecasting and Data Analytics

Capability to forecast future condition, cost and functionality and risk. Optimised intervention times determined based on life cycle. Scenario based analysis to explore the best options for a given budget.

CAMS Mobile iPad App Offline Field inspections

Inspection data collection app with building and inspection data sync with CAMS cloud.  Built-in data validation and GIS.

The CAMS Advantage

Currently, CAMS covers buildings, drainage assets and bridges. This is being expanded to other general asset classes such as footpaths and park assets as well as road pavements.

Scenario analysis, the level of service, cumulative backlog and asset risk analysis provides the practitioner with comprehensive tools for decision making.Integration with a business intelligence tool provides the user with the ability to customize and generate their own reports live by combining different parameters.

New modules in development include energy retrofitting of buildings, disaster resilience of bridges, automated inspections using RFIDs and building information modelling.

  • Data driven approach to decision making
  • Reduce inspection cost and improve accuracy
  • Reduce inspection frequency and associated recurrent costs
  • Facilitate proactive preventative maintenance
  • Prevent catastrophic failures of assets and service interruptions
  • Reduce risk cost
  • Reduce overall asset management costs by considering the life cycle of assets
  • Robust deterioration prediction algorithms backed by academic research
  • 9 level hierarchical division of buildings for finer grained data collection
  • Support for function based rating (most systems only support a condition rating)
  • Data collection iPad app
  • Database management
  • Data exploration
  • Deterioration prediction
  • Budget calculation
  • Backlog estimation
  • Risk management


High-level workflow that depicts the key steps involved in running an analysis in CAMS
  • 1

    Create Hierarchy

    Create an asset component hierarchy based on the IPWEA convention. For example, the hierarchy for buildings includes levels Building Type, Building, Building Functional Area, Component Group, Component Type and Component. The use of the entire hierarchy is not compulsory and the user can set up the hierarchy based on the levels used in his/her user organization.

  • 2

    Upload Component Data

    Once the hierarchical data is set up, the user can upload the component data into CAMS. This can be achieved by individually entering the components or by importing in bulk via an Excel template.

  • 3

    Assign Condition Data

    Inspection data (i.e. condition rating) for each of the components or component types can be imported via an Excel template or by using the CAMS Mobile application.

  • 4

    Assign Transition Matrices

    Transition matrices define the deterioration trend of different asset components which have been derived using condition data on similar assets. Deterioration prediction uses a Markov Chain approach utilising the assigned transition matrices and provides probabilistic estimates of a future condition of an element. A default set of transition matrices are provided with the system and users can edit or enter new ones to improve the forecasting algorithm.

  • 5

    Run Forecast Modelling

    Once the above data is available, user can run the different forecasting functions under “Deterioration Prediction”, “Sustainable Decision Making”, and “Economic Forecasting”.

A scientific approach to asset management

Robust deterioration prediction algorithms backed by over 20,000 hours of academic research