Why is sustainability important, especially in design and construction? And can AI (artificial intelligence) help build more sustainable cities and infrastructure of the future? These are the questions we must be asking as we design and build the cities, homes, and infrastructure of tomorrow.
Last week, we kicked off this blog series looking at resilience in construction, defining the term and identifying how technology such as AI, and setting aside all the hype, can help build more resilient cities and infrastructure of the future.
Today, let’s look a bit broader, at sustainability in general and in the construction industry. What exactly does this mean? That is a great question, and something that must be explored first.
Defining Sustainability
In my book, Sustainable in a Circular World, I point to Merriam-Webster’s definition of sustainable as relating to or being a method of harvesting or using a resource so that the resource is not depleted or permanently damaged. Simply put, it is environmentally sound living without compromising the needs of future generations.
In construction, this means can mean many different things. As one example, we must consider a building’s operational carbon—or the carbon load created and released during the use of energy to heat and power a building—and embodied carbon—the energy released during manufacturing, production, and transportation of the building materials. There is the building itself and there is what it takes to create the building.
If we want sustainable cities, and therefore if we want businesses, cities, governments, and individuals to be good eco-friendly citizens then we need to understand the constant journey of change—and this is precisely where technology enters the conversation.
AI, Sustainability, and Construction
Let’s consider this with a few examples. In June, Monash University in Australia announced a new AI system that can automatically identify contaminated construction and demolition wood waste.
Why is this important? Contaminated wood from construction and demolition sites often ends up in landfills due to the difficulty of sorting it manually. Most of it can actually be recycled, but contamination from paint, chemicals, metals, and other construction residues makes sorting extremely difficult and costly.
By applying AI models, the team found strong precision and recall across six types of wood contamination. Ultimately this led to better waste-management practices, improved circular-economy goals, and a global push for greener construction. Again, this AI was all about having AI working in tandem with people to reconcile good decision-making for a more resilient future.
As another case, we see Northumbria University in the United Kingdom has secured £250,000 of research funding. The research will look to improve waste recycling by leveraging AI (artificial intelligence) to track waste generation, optimize resource usage, and provide construction managers with key data to make better decisions. Further, KPIs (key performance indicators) will measure waste handling efficiency, resource utilization, and adherence to sustainable practices.
This is all about minimizing waste in landfills and promoting circular economy principles. And it is done with good, clean data so construction managers can make informed decisions.
In my book, I sound the alarm that digital transformation, technology, and the digital feedback loop must be used to address the sustainability challenges we face today. Although, ultimately, it is data. Data is key. Data will provide contextualized insights. Data will help regenerate the natural ecosystem. But as I said last week, this only works if we have good, clean data and the people to help understand it and interpret it.
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