Australia is embracing a significant wave of infrastructure projects aimed at enhancing the nation's livability

Spanning from projects like the METRONET in Western Australia—which envisions the construction of 72 kilometres of new passenger rail and 23 additional stations—to endeavours such as the North East Link in Victoria—set to connect Melbourne’s freeway network—these efforts represent a surge in developments created towards improving the quality of life across the country.

Earlier in May, the infrastructure community gathered at Bentley Systems’ Illuminate event to spotlight how the industry is bolstering its potential to keep up with the development underway across Australia.

For instance, Julie Jupp, associate professor of digital engineering at the University of Technology Sydney, articulated the complex juncture at which the industry is currently. She discussed how not only are engineering professionals underprepared to apply their knowledge in the market, but also how the fast evolution of technology is demanding a shift in how educational institutions conceive training and development programs.

Jupp further highlighted how the integration of technology systems within training programs are an important way of addressing the current skills gap that plagues Australia, as well as ensuring the labour force is equipped to fully realise the powerful technological solutions at their disposal.

Technology, and more specifically AI, undoubtedly served as the central theme in discussions about solutions to industry challenges. However, there is widespread recognition of the fact that to fully harness the potential of AI, there is first a need to address key data challenges. Below are the top three priorities that organisations in the industry must tackle to optimise the use of AI.

Better infrastructure projects start with better data

For decades, the infrastructure project lifecycle was linear. Planning, design, procurement, construction, and operations had distinct phases with individual stakeholders, requirements, and discrete hand-offs. Each phase typically used different technologies and processes. This approach created information silos and data loss, resulting in design rework and errors, project delays, and increased costs and risks.

As infrastructure projects and their respective phases have become more interconnected, there is a real need for bringing engineering, information, and operational technology systems and data together. But the reality is that an abundance of valuable data from these technology systems is trapped in files, models, drawings, and even paper. Unlocking this data is critical to better decision-making across the infrastructure project lifecycle, as well as for using AI-powered solutions.

Instead of generating critical project and asset data in disparate systems, infrastructure engineers, construction contractors, and owner-operators should start producing data layers with open platforms that generate digital twins.

Infrastructure digital twins unlock data and process silos

Over the last few years, digital twins have become a hot topic across every sector. They have evolved into a powerful and valuable way to combine and leverage data from disparate sources and multiple disciplines into a holistic, dynamic representation of infrastructure projects and assets. In addition to providing a structured way to bring siloed data together, digital twins can unlock data from existing design files, essentially “lighting up dark data.”

When digital twin capabilities persist across the infrastructure project lifecycle, they create workflows that enable engineers to seamlessly conduct design reviews, structural analysis, and calculate carbon footprints. Digital twin workflows can help construction companies improve the accuracy of quantity take-offs and project scheduling. Just like their value in bringing data together, infrastructure digital twins can connect processes between the different lifecycle stages of a project and asset.

As discussed at Illuminate, Victoria’s Level Crossing Removal Project (LXRP), led by WSP, faced challenges that included tight rail corridors, preserving heritage sites, and managing various construction tasks. To tackle these issues, WSP created a digital twin using advanced technology, which helped coordinate activities, detect issues, and engage stakeholders through virtual tours. This approach saved 300 resource hours, cut modelling time by 60%, and reduced the carbon footprint by 30%, showing how digital twins can improve project management and environmental sustainability in infrastructure projects.

As data layers are combined and processes are connected, their collective value is compounded exponentially. It is this value that provides the foundation for quickly and easily applying AI techniques and technologies to drive actionable insights and infrastructure better outcomes.

The potential of AI for infrastructure projects

Certain AI techniques and technologies are already being used in the infrastructure sector in Australia. For example, Transport NSW is using artificial intelligence to improve road maintenance and operations. Sensors and cameras on council vehicles collect real-time data about road conditions, which AI analyses to quickly identify and address issues. This approach helps make roads safer for drivers and pedestrians by streamlining maintenance tasks and fixing problems faster.

In a sector challenged by resource constraints, delays, cost overruns, and evolving sustainability requirements, engineering and construction applications with generative AI capabilities have the potential to automate tasks, streamline workflows, improve project delivery, and ensure asset performance.

Consider this: generative AI technologies could give engineers the ability to collaborate with AI agents to generate and optimise infrastructure design. They could compare designs to previous ones and learn from an engineer’s choices. Generative AI could help engineers and construction managers use more sustainable building materials to reduce an asset’s carbon footprint or calculate an infrastructure project’s embodied carbon from start to finish.

Generative AI will give infrastructure engineers, construction contractors, and owner-operators the ability to experiment with real-life developments in a simulated environment, enabling more predictive activities and outcomes that will mitigate future challenges and risks. That potential is too valuable to ignore.

A roadmap for today and tomorrow

 Can AI address infrastructure project challenges in Australia? Yes. However, there is still foundational work that needs to be done. The simplest and fastest way for infrastructure organisations to get started with AI is by focusing on how data is organised, managed, used, and unused. Organisations that master data management and leverage digital twins will be best positioned to unlock the potential of AI—and whatever new technologies lay ahead.

By Rob Malkin, Senior Regional Director of Bentley Systems in Australia and New Zealand

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