Predicting industry automation

Preparing for AI, machine learning, and automation can make even the most informed industry members uneasy. With technology evolving so quickly, predicting our uncertain future is challenging, but in many ways, it’s possible.

The Building Research Association of New Zealand (BRANZ) anticipates that technology, AI, and automation will significantly transform the nation’s construction industry over the next decade. Manfred Plagmann, principal scientist at BRANZ, admits it is a challenge to predict the future, but doing so is possible, especially when it comes to consent and design.

Machine learning is set to revolutionise the industry by making the consenting and compliance processes faster and more accurate. “AI will be able to detect errors in documentation on both the design and regulatory sides,” Manfred says.

He adds that as artificial intelligence evolves from generative to predictive models, the compliance pathways used today, such as how standards and project deadlines are met, may be phased out in favour of more advanced modelling processes.

Progressing with AI

In practical terms, instead of setting schedules and adjusting them as the project progresses, AI would analyse vast datasets to suggest the most efficient project path, predict possible issues, and ensure standards are met, all in real-time. That’s how generative and predictive AI differ: both generate ideas, but the latter also forecasts outcomes and offers choices.

“This will enable the assessment of building performance early in the design phase, allowing designers to understand the performance and cost implications of their decisions more efficiently,” Manfred adds. These processes are technically feasible today but are often too time-consuming and costly.

He predicts that AI will also play a crucial role in the construction phase. AI tools can help ensure that buildings are optimally designed and built to realise their full potential. However, as AI and machine learning are data-driven, progress requires the correct infrastructure. AI systems don’t generate new knowledge independently; they rely on high-quality information. The construction industry must actively contribute accurate data to make the most of this technology.

“A centralised repository for all consenting documents will be essential, not only as a foundation for AI learning but also for broader applications, such as housing stock models and others,” Manfred says. “This would allow stakeholders to analyse the effects of regulatory changes on supply chains, the

workforce, skills, and training needs. Additionally, businesses could forecast market opportunities for retrofitting and other products.”

Exploring potential

BRANZ is actively exploring AI capabilities within the industry. One example is a collaborative project with Auckland Council and the Fraunhofer Institute for Building Physics in Germany that examines whether AI can help in the consenting process. BRANZ fields many council queries that require reports to be clarified, indicating a need for clearer and more accessible results. The project aims to identify bottlenecks, assess how AI could assist consenting officers in interpreting computer-simulated building performance reports, and encourage industry and building control authorities to use simulation tools. The research will contribute to a framework ensuring accurate simulation conditions and quality outputs, thereby increasing councils’ confidence, reducing construction risks, and encouraging innovative materials.

Another example is a University of Auckland-led project that uses machine learning to predict metal corrosion in climate change scenarios. The project analyses past BRANZ data using AI to develop dynamic models that can predict the corrosion severity of metals and create a ‘digital corrosion map’. This project is connected to wider BRANZ-led research that is developing a full picture of how new materials perform under the changing climate. All AI research contributes to a stronger understanding of how the technology can improve industry processes.

New Zealand’s construction industry is already taking proactive steps to improve efficiency, compliance, and risk management to embrace AI, machine learning, and automation. By investing in advanced tech and infrastructure, collecting high-quality data, and upskilling the workforce, the construction sector is well-positioned to capitalise on artificial intelligence ethically and wholeheartedly.