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Construction maintenance using AI-driven insights

Posted on 19 May 2025 by Dr Miguel Balbin

Andrew Hannell, founder of in ����ɫ��, South Australia, has been a long-time advocate for integrating digital technologies into the construction industry. With over 25 years of experience in architecture, engineering, and construction, he aims to leverage digital tools to enhance infrastructure assessment, reduce risk, and improve on-site decision-making.

One of the greatest challenges in his industry is the difficulty of visually assessing and documenting damage and potential hazards on infrastructure projects. Hannell’s interest in this area was initially piqued by the on South Australia’s Fleurieu Peninsula, which needs to be regularly monitored to assess the condition of deteriorating tracks and to determine whether nearby vegetation, such as dried weeds, poses any fire risk.

Relying solely on human inspection introduces subjectivity, which could lead to inconsistent evaluations, reporting inaccuracies, and unnecessary increases in maintenance costs.

“Construction is a very expensive, very conservative business,” said Hannell. “During both construction and operational phases, there are many, many inspections that are required. On many projects, that’s done entirely manually. [It’s often] someone walking around with a clipboard.”

“Something as simple as counting potholes… and recording where they are could save millions, or hundreds of millions of dollars,” he continued. “If that can be automated, it will save a huge amount of time [and] add other benefits.”¹

Hannell recognised the potential for AI-driven defect detection to bring greater objectivity and consistency to infrastructure assessments, pinpointing not only the location of defects but also evaluating their severity based on measurable criteria.

To explore this potential, he collaborated with AIML engineers Sam Hodge and Aaron Peter Poruthoor in 2022 to develop a practical solution tailored to the realities of infrastructure monitoring.

The team committed to building a minimum viable product (MVP) within a 12-week sprint, resulting in ConstructAI, a camera-based machine learning platform that uses computer vision to automate critical infrastructure monitoring procedures when mounted on the front of a locomotive. Using data derived from SteamRanger footage provided by Hannell, the team trained the model to accurately detect and classify various issues.

“Based on testing during development of the MVP, the key benefits  included rapid data collection that was many magnitudes faster than alternative methods,” said Hannell. “The tool also produced  high-quality data that was non-subjective,”

Following this initial success, the MVP was refined over several phases of iterative testing to improve accuracy and reliability.

From the outset, AIML designed the system to keep Hannell and his team in the loop. The software was built to be accessible and easily maintained over the long term, and included thorough documentation to support future adaptation and development.

While ConstructAI has not yet been deployed commercially, Hannell sees enormous potential for its application, especially given the growing interest in artificial intelligence (AI) and machine learning even in the conservative construction sector. His collaboration with AIML has also provided a valuable framework for future AI-driven innovation in the sector.

“Working with AIML was a great experience. The engineers were practical and flexible, and we worked collaboratively on the project,” he said. “Although both the initial concept and final developed solution were quite simple in technical terms, AIML introduced valuable ideas and innovations to the process,” said Hannell.

“On a personal level, I learnt a lot and enjoyed working with AIML.” He continues to advocate for AI’s ability to significantly improve safety and reduce costs in his industry.²

Sam Hodge, one of the engineers on the project, echoed the positive sentiment.

“Andrew [Hannell] was a dream customer,” said Hodge. “[He] understood the value of good data and that the simple things can often be the best value for the user story that needs to be solved.”

“He took an off-the-shelf computer vision model and applied it to a real-world problem of maintenance of infrastructure that would have been prohibitively expensive to do manually,” Hodge continued. “The automation means that maintenance of the heritage railway can continue far into the future.”

In June 2025, Hannell participated in a video of AIML collaborators as part of AIML’s Industrial AI Program launch event. In the video, he encourages small and medium enterprises (SMEs) to explore using AI in their operations.

“Start at a purely business level,” he said. “What costs [you] the most money? What takes the most time? Where are the biggest risks? Where are the biggest opportunities? [Then] work backwards from there. The answer or potential answers to those sorts of issues can certainly be found in AI.”¹

References

1. ‘Launching the Industrial AI Program’, Australian Institute for Machine Learning, 
2. ‘Why is AI important to construction?’, Digital Constructors Blog,

Technology so good it can predict shadows: AIML's deep learning enhances 3D maps offered by SA company Aerometrex

Originally published in AIML's Machine Learning Capability Report, December 2021

As part of the South Australian government’s 2019 program of investment in SMEs, AIML worked with geospatial tech company Aerometrex to create enhanced 3D data products for clients in city planning, development, urban design and regional councils.

����ɫ��-based Aerometrex’s high-resolution 3D models are ideal for developing new artificial intelligence and machine learning products.

AIML worked with Aerometrex to boost mapping products with deep learning capability. The new technology is so detailed it reveals shadows cast by buildings at different times of the day, and the heights of individual structures.

“Aerometrex remains at the cutting edge of global 3D modelling, and through this project we are taking our solutions to the next level,” says Fabrice Marre, Aerometrex’s Geospatial Innovation Manager. “AIML are helping us solve real-world problems.”

Enhanced 3D maps are expected to become the standard tool for developers and planners around the country who are making decisions around what developments should go ahead, or what designs need to be modified.

“Shading caused by a building is one of the big factors councils consider when approving new buildings,” says Marre. “With our maps, city planners are able to see with much more detail exactly how the surrounding areas are going to be impacted.”

Other industries stand to benefit too. Solar installers will be able to assess more accurately the optimal layout for solar panels on a roof, landscapers will be able to see where and how shade from large trees will fall at different times of year, and property developers will be able to better plan their projects to maximise the use of space.

Deep learning for better maps

While Aerometrex had developed capabilities for labelling items in 3D maps, their initial approach was restricted by the amount of data that could be labelled, and relatively weak software capability.

“We wanted to create maps with infinitely more detail, and to provide our customers with maps that enabled them to search for very specific information,” says Marre. Aerometrex pulled in AIML’s expertise to apply deep learning to create a solution. Machine Learning Engineer Sam Hodge was the AIML lead on the project.

“Because Aerometrex already had so much information, ‘teaching’ the algorithm was relatively straightforward,” says Hodge. “We were able to take all the information they already had, break it down into as much detail as possible then build a model that matched it all together.”

The beauty of the approach lies not just in the amount of detail captured, but in the scalability.

“Once we have ‘taught’ the algorithm enough, we no longer need to label every object,” explains Hodge. “The algorithm will keep learning for itself, drawing context and information not only from individual pixels but those surrounding it, and the tens of thousands of other pictures it has examined.”

The end result is a 3D map that contains an incredible amount of derived information. Users are able to search areas for specific detail – from single blocks to entire suburbs or cities – that can help them make better decisions whether it’s related to planning, developing, building or marketing. AIML continued to work with Aerometrex in 2021.

Established in Australia in 1980, Aerometrex has a strong national and international reputation as a leading practitioner of aerial imaging, photogrammetry, 3D modelling and LiDAR surveys. The company provides professional, accurate digital image mapping and geospatial engineering solutions for clients in government and the private sector. In 2020 Aerometrex had its strongest financial year to date, launched Aerometrex USA and grew its workforce by 33 to reach a total of 116 employees.

How machine learning expertise helps small banks keep customers safe

Originally published in AIML's Machine Learning Capability Report, December 2021

As part of the South Australian government’s 2019 program of investment in SMEs, AIML worked with Lot Fourteen-based business automation company Neo-Analytics to create smarter software for regulatory compliance monitoring in banks and financial institutions.

Adherence to strict regulations and standards is vital for banks to keep financial risks low and ensure safety of clients’ money. However small financial institutions with few staff and limited technical capability can feel overwhelmed by this burden of compliance.

Machine learning offers a solution.

“One of the requirements of operating a financial services institution is to follow local and national government laws and regulations – this is called regulatory compliance,” said Rick Rofe, founder at Neo-Analytics.

“This is a huge job involving lots of data, and so we worked with AIML to develop automated processing capability for regulatory compliance.”

Neo-Analytics now applies algorithms that deliver reliable and accurate results, providing smarter more intelligent software for their client base.

“We are now working with three banks, one of which has our products in production,” Rofe said. “We hope these improvements will soon allow us to employ our contractors permanently as adoption of our machine learning products increases.”

High burden for small institutions

Financial services institutions include banks, building societies and credit unions – these are regulated entities that can carry on banking business, including taking deposits from customers.

Such institutions are compelled by law to have robust and accurate measures in place regarding risk, including detection, measurement, reporting and management. The implied dollar amount for regulatory compliance totalled A$5.4 billion in 2016, representing 24% of community bank net income.

Data management tools like Excel don’t cut it for financial management anymore – institutions need new approaches that offer analytic flexibility, scale and automation. For smaller institutions, costeffectiveness is also vital.

AIML applied machine learning models to improve Neo-Analytics’s regulatory compliance monitoring.

“AIML was instrumental in modelling our compliance needs, and they assisted our developers to implement machine learning algorithms from scratch,” said Rofe.

“AIML used the data we made available to them, and built a predictive model for regulatory compliance that proved to be accurate and met our requirements.”

Neo-Analytics is a finance RegTech innovator focussed on artificial intelligence and machine learning, credit monitoring and data management. Based at ����ɫ��’s Lot Fourteen innovation precinct, Neo-Analytics helps financial institutions survive and thrive in a market of accelerating and complex change – a market that has become exponentially more challenging due to recent global crises. Their primary focus is around AI-driven features that solve real problems for customers and creating better outcomes.

Yes, I'll endorse that: how AI helps ����ɫ�� start-up Pickstar match celebrities with promotional opportunities

Originally published in AIML's Machine Learning Capability Report, December 2021

As part of the South Australian government’s 2019 program of investment in SMEs, AIML worked with start-up Pickstar to apply machine learning and data analytics in a technology platform that matches customers with celebrities for promotional opportunities.

Pickstar is an SA business that allows customers to use an online form to pick from a range of celebrities to be guest speakers and brand ambassadors.

CEO and founder of Pickstar James Begley said AIML has been helpful in providing a service that will be able to effectively pair up customers with the right stars within the client’s budget.

“The question that we answer as a business is, who can I get for my budget?” Begley said.

“The work that the AIML does allows us as a Pickstar platform to serve up and recommend the best available talent for someone’s brief and budget and that can only happen with heavy investment into machine learning and data analytics to underpin the recommendation engine.”

Begley said machine learning will help Pickstar to achieve faster and better results for their customers.

“AIML have provided us a road map but also a prototype, an actual tangible early-stage product that we are going to use and commercialize – this is taking university smarts and bringing it into the real world for commercial application,” he said.

“For us the investment into machine learning and data analytics is only going to increase, so if we can maintain that relationship with the institute we will be very pleased to follow on.”

Data is king

Dr Grant Osborne, the Lead Machine Learning Engineer at AIML, said Pickstar could see how important data is and how it can be applied to improve the experience for everyone involved.

“The guys at Pickstar are really on it when it comes to seeing that data is important, and knowing if we have this kind of opportunity available for a talent these are the most appropriate jobs,” Osborne explained.

Osborne said they try to make the process simple and understandable for companies like Pickstar.

“We build demonstrators that we can put straight in front of the clients, they’ve got a demo app where they can see all the data and see what the predictions and recommendations type engines will be able to do for them,” Osborne said.

“We’ve been working with Pickstar to essentially help them build a more data-driven business. We’ve been looking at data sets, helping to identify the most important element of the data, building dashboards as well as doing machine learning around prediction and the kinds of talent they will be recommending for certain jobs. Data is king.”

Based in South Australia, Pickstar was started in 2013 by former AFL players James Begley and Matthew Pavlich. The platform hosts a database of sports players and celebrities that is searchable by potential customers seeking to book a star to speak at an event or endorse a product or brand. Pickstar recently opened offices in the USA and the UK to capitalise on new global opportunities with major sporting institutions.

Blockbuster AI behind the perfect Hollywood face

Originally published in AIML's Annual Report for 2021 by Kurtis Eichler and Eddie Major

In 2020, millions of people across the globe experienced the very latest in South Australian AI technology in an industry worth more than $100 billion. And if it all went according to plan, they probably didn’t even notice it at all.

AIML researchers teamed up with ����ɫ��-based visual effects company Rising Sun Pictures (RSP) to use AI to create effects for some of Hollywood’s biggest blockbusters, including Marvel Studios’ Shang-Chi and the Legend of the Ten Rings.

This involved creating an AI method of replacing the faces of stunt performers in combat scenes with those of the lead actors. Computer vision researchers Dr Ben Ward and Dr John Bastian worked on the film with RSP, before joining the VFX studio as full time employees in October.

“Rather than the traditional 2D and 3D face replacements typically used in highintensity action scenes, the team used an AI deepfake method,” Dr Ward says.

Deepfakes are synthetic media where a person in an existing image is convincingly replaced (or faked) with the likeness of someone else, using deep learning — a type of AI that learns from data and uses multiple software layers inspired by our brain’s own network of neurons.

For Shang-Chi, this involved around 30,000 facial images across five characters, training five machine models in more than four million training iterations. The models were used for around 50 face replacements in six key action scenes.

“AI can help artists accomplish incredible artistic effects without the tedium of executing every frame in a long sequence,” Dr Bastian says.

Dr Ward and Dr Bastian are currently using their facial replacement method on three new film projects.

Tony Clark, RSP’s managing director, says AI can speed up delivery of visual effects and relieve artists of tedious, time consuming work.

“AI holds great promise for visual effects applications, especially in terms of accelerating labour-intensive tasks and augmenting human creativity,” Clark says.

Our early work with AI has produced spectacular results and we are eager to push development further.” While AI might help bring the creative magic of Hollywood to life, the reality is show business is exactly that, business.

For RSP, Clark says the collaboration with AIML helped the company generate $1 million in increased revenue with an additional $3 million forecast in 2022.

“Collaborating with AIML has enabled us to continue to deliver amazing images, and reinforces to global studio executives that RSP is among the best in the world at embracing and implementing advancements in technology such as AI,” Clark says.