Digital Tools and Technology-Implications for Improved Health & Safety in Construction

Dr Ross Trethewy, Head of Health Safety Environment, ADCO Constructions

The Challenge:

The Australian Bureau of Statistics identifies the construction industry as a generator of $360 billion in revenue each year, or 9 percent of GDP, with an estimated growth of 2.4 percent over the next 5 years, which will continue to increase. But the industry has traditionally been low in its investment in research and development at 1.4 percent of net sales, compared to 4.3 percent in the aerospace industry and 4.7 percent in the financial services sector. More recently, a 2025 study in the state of digital adoption in the construction industry in Australia has determined that twenty-five cents of every dollar invested is directed towards new technology, a one-third increase over 2023.

This upswing in expenditure on new technology and associated improvements to design, buildability and project delivery through Modern Methods of Construction (MMC) is producing associated flywheel effects that improve time, cost, quality health and safety and other risk management outcomes.. Paradoxically, the most recently published statistics for the Australian construction industry identify that worker fatalities remain a significant concern, and in 2023, the number of fatalities in the construction sector was 36 percent higher than the 5-year average, with 45 fatalities nationally. Safe Work Australia statistics show that the Australian construction industry’s fatality rate is more than double the national average.

The continued demand for construction services globally and international research implicating design and planning as a key cause of fatality, considered in the context of the above statistics, supports the need for a disruptive shift from traditional construction delivery methods to MMC, supported by digital tools and technology.

Case Study

The use of digital tools and technology to support MMC was evaluated on a construction project in the Northern Rivers Region of NSW in 2025. The project involved the replacement of school classrooms across seven schools damaged as a result of multiple flood events in the region.

The regional nature of the project, scarcity of local suppliers of construction materials and labour, and the region’s susceptibility to weather events prompted the use of prefabricated modular classrooms manufactured in a factory within Australia. The factory utilises Building Information Modelling (BIM) workflows interfaced with Revizto to create a Digital Twin.

“Rapidly emerging AI machine learning is likely to be a key element in fishing from the data lake very soon”

The Digital Twin programs shopfloor robotics to manufacture each modular classroom. The buildings were then transported by semi-trailer to each project site and lifted onto preinstalled first-floor steel framing foundations to enable flood mitigation heights to be achieved.

The project identified significant benefits:

• Productivity: each classroom was built in a controlled factory environment operated over a 24 hour day using advanced shopfloor robotics supplemented with human labour.

• Predictability: the factory production meant that time, cost, quality and health and safety were significantly enhanced over traditional work methods on a project site.

• Reliability: the factory was free of disruptive factors such as weather, labour and material shortages, maximising production.

• Program: factory prefabrication meant other critical path activities including ground works, foundations and essential services could be done in parallel.

• Health and Safety: risks typically associated with traditional ‘stick built’ construction methods, e.g., work at height, were substantially reduced, in some cases up to 80 percent.

• Quality: The factory environment meant significantly fewer defects emerged at the project site.

Discussion

The case study provides insight into the benefits of digital tools and technology emerging within the construction industry and flywheel benefits to health and safety, cost, time, quality and other key elements of construction risk management. The use of BIM and its related collaboration tools e.g., Revizto is commonly used on large construction projects, but more recently have been used for temporary works design and high risk construction work activities related to permanent or temporary works.

Uses include 4D modelling, with time-based scheduling to sequence the simulation model in high fidelity enabling visualisation of construction sequencing. The modelling is typically used to inform workers and supervisors involved in high risk construction work

Images from 4D sequencing modelling for high risk construction work installing mega-columns at Martin Place Metro Project

The challenge for traditional health and safety practitioners and principal contractors with management of a construction project is measuring the tangible benefits to health and safety from construction design, planning and logistics using MMC, typically executed seamlessly using digital tools and technology without, significant incidents of injury or defects.

The advent of BIM and collaboration tools like Revizto, when considered in combination with:

• semi-autonomous and autonomous robotics used for high risk construction work;

• project site cameras;

• cranes and mobile or static plant, computers and operating data;

• event-based scheduling; and

• other IOT data related to sensors or other digital devices;

Creates a myriad of ‘digital threads’ across the many activities that occur on any construction project.

The challenge is classifying data threads to enable a Common Data Environment (CDE), i.e., Data Lake, as a single source of truth for all project information, so all project team members or collaborators can access the most current and accurate data.

For a construction project, a CDE or Data Lake is already real. This means that construction project management issues that would typically arise such as out of sequence works related to a quality defect or a late design change; and implications for works scheduling, health and safety or other risks, can now be more readily evaluated and managed through a digital design and IOT data framework. In the past the inherent links between construction program, quality, cost, design change and other key factors in construction management and health and safety risks have not been well understood This has been further compounded by the use of downstream lag indicators as a measure of health and safety performance. The ability to fish from the Data Lake for real time predictive data using Ai machine learning is likely to be the next evolution in risk management.

The interrelationships of data threads should not be underestimated in the broader concept of health and safety or other management disciplines required for the successful delivery of a construction project. For example, fixed cameras in the past are typically associated with security, but technology advances now include features like biometric scanning, productivity management, vehicle clash detection, artificial intelligence and connectivity, all within a digital environment. Hence a rich source of analytical output can be derived from real-time site data sources.

Conclusion

The steady increase in digital adoption in the construction industry means that the ability to fish from the Data Lake and identify elements of ‘predictive analytical data’ is available now to large scale construction projects.

The case study demonstrates simple and tangible benefits of MMC using BIM and Digital Twin technology. Prefabrication of modular classrooms meant that key health and safety risks typically associated with traditional ‘stick built’ construction methods were substantially reduced, many by a factor of 80 percent.

At an enterprise level use of digital tools and technology and related data threads and their interrelationships discussed will ultimately provide predictive analytics across many disciplines of construction design and delivery management including health and safety. Rapidly emerging AI machine learning will be a key element in fishing from the data lake very soon.