Emerging technologies like visualisation and rendering software powered by AI and machine learning enable architects to assess sustainability early in the design process, writes Dan Ring of Chaos
Climate change is top of mind for everyone, and with 2025 projected to be among the warmest years on record, architects and city planners face increasing pressure to prioritise sustainable design. Without proactive planning, communities could be left vulnerable to extreme temperatures, rising energy costs, and flooding.
The National Flood Risk Assessment (NaFRA) reports that around 6.3m properties in England are already at risk of flooding. As the climate continues to change, that figure could rise to 8m by mid-century.
At the same time, many British homes are ill-equipped to handle rising temperatures and operate efficiently. These realities call for the implementation of sustainable design and construction in the country. It’s not only about comfort but about safety, resilience and the future of how we build.
While the government has promised £650m to “sustainably upgrade” public buildings, where does that leave homeowners and families? Possibly paying for an £11,000 heat pump.
The question arises – how economically feasible is sustainable construction? Renovating existing buildings or building sustainably from scratch can oftentimes be an expensive and tricky process, but technology can help alleviate the costs and empower architects to design smarter cities.
How machine learning can reduce construction costs
Construction can be expensive but digital design tools built with emerging technology like AI can significantly reduce costs. Many of these tools leverage machine learning, a subset of AI that uses models and algorithms to generate insights and support smarter design decisions. In the earliest stages of design, machine learning empowers architects to test the sustainability of a building before construction begins, exploring the “what ifs” much more quickly.
For example, using ML-powered tools, an architect can rotate a building 10 degrees and instantly see how that change impacts thermal performance. This early insight helps pinpoint potential pain points and optimise energy efficiency before breaking ground.
By identifying issues upfront, teams can minimise redesigns, reduce the amount of material waste during construction and ensure a more efficient construction process.
Machine learning also frees architects to spend more time on creative ideation by automating repetitive, time-consuming tasks. Faster iteration at the early pre-design stages can help predict environmental impact sooner.
For example, spotting unexpected anomalies in embodied carbon or thermal comfort.
Uncovering requirements with their clients like this cuts down on frustrating turnaround times and gets to better answers faster. Some AI tools can also sense-check design decisions to make sure that they comply with local codes and regulations while also staying true to client requirements.
When looking at sustainable construction, the choice of materials is vast, and sometimes paralysing. When matching intent to low-carbon options, it can be easy to fall back on old favourites even though more suitable (and cost effective) solutions might be available.
By focusing on your specific project requirements and budget, AI-assisted research helps make identifying and sourcing alternative materials easier, while also evaluating any potential risks.
Importantly, it’s worth noting that sustainable construction may not be as expensive as some claim. Research shows the upfront investment is often modest, only around 2% higher than traditional construction.
Over time, however, these buildings pay for themselves, cutting operational costs by as much as 14%–19%. By improving insulation, adopting passive cooling techniques, enhancing energy efficiency and integrating renewable energy, homeowners and developers alike can create spaces that are not only better for the planet but also more affordable to run.
Empowering architects and expanding access
Climate change isn’t going away, and the built environment must continually evolve in response. Beyond advocating for sustainable design, architects need practical, powerful tools that help them turn ambition into action.
This is where AI and machine learning are beginning to make a real difference.
Emerging technologies like visualisation and rendering software powered by AI and machine learning enable architects to assess sustainability early in the design process. Working as creative companions, they can simulate a building’s environmental impact before construction begins, making smarter decisions about materials, energy use and efficiency throughout the construction process.
Machine learning doesn’t just improve design outcomes; it makes sustainable construction more affordable and accessible. By streamlining workflows and cutting iteration time, these tools can reduce design and construction costs, empowering architects and developers to bring sustainable design to more communities.
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