Dec 02

Intelligent Design

It comes as no surprise that the high-tech, telecoms and financial services sectors are the leading early adopters of machine learning and artificial intelligence.

After all, these industries are well-known for their willingness to invest in and integrate new technologies, in order to gain competitive advantage and achieve internal process efficiencies. But it is painfully noticeable that the construction industry is missing from the list.

Why is it taking so long for construction to catch up with advanced technology? As an industry, we amass huge amounts of data – but it is only now that we are finally starting to understand what we might do with it and where it might take us.

Take, for example, machine learning: this technology might allow us to generate progressive and independent thinking within the machines and computers that drive our industry. In other words, machines are learning to think for themselves, but it is only by feeding them vast quantities of data that we can enable them to learn. From there, computers might analyse that data, to predict patterns and behaviours within occupied buildings and help designers to make more informed choices in the design and prototype phases of a project.

Machine learning is a branch of the wider theory and development of computer systems known as artificial intelligence or AI. It has many potential applications in construction for the design and operational stages of a project, but it is perhaps building operations that might be our best target. In effect, it is about giving a building a brain. Machine learning operates in a very similar way to the neurological processes of the human brain. By looking at patterns within data, it can make informed decisions on what will most likely happen next and adjust accordingly.

When a child eats sugar, an electrical signal is fired to the brain that says, ‘I like this’. Over time, when a child eats more sugar, this emotion is triggered again, and over time, a neurological connection becomes burnt into the brain. The brain has essentially been ‘trained’. This is very similar, broadly speaking, to how machine learning and neural networks work. The main difference is that a machine gathers information from large existing data sets much quicker than a human can.

If a computer programme sees thousands of architects selecting a particular type of door handle – for use on a particular door type, in a particular building type, in a particular country – it can use this knowledge to make future recommendations to architects automatically about which door handle they might select. It will also know why it is making that recommendation; its selection is based on the choices of previous architects.

This is why many software companies are transitioning their platforms to the cloud so that they can start to combine information from many different data sources and connect the dots. The development of smart buildings is on the rise, due to the availability of the cheap sensors and connected devices that make up the Internet of Things (IoT). These are becoming commonplace in our homes, with many everyday gadgets and appliances now available as smart products. Likewise, many buildings already have sensors that measure light, heat, energy consumption, footfall, movement, capacity and more. Many are part of proprietary, closed systems that do not talk to each other, but this is changing as the systems become increasingly able to share data in open standard formats or via application programming interfaces (APIs).

Once data is connected, we can then layer on top those machine learning algorithms that allow us to predict the behaviours of a building and its occupants, not only for efficiencies in operating and maintenance but also to inform the way we design future buildings to optimise space and flow and select more appropriate building materials.

Equally, many clients are now asking to know when an asset is likely to break down so it can be fixed or replaced ahead of time. Once we have enough data, we can use a linear regression algorithm on the data to predict asset failure. Machine learning is also used in more everyday tasks, such as providing more relevant search results, by using previous user search trends to influence results. This technique has been used for years by companies such as Facebook or Netflix, which use machine learning to push news stories and recommendations to your feed.

With many buildings now been designed, delivered and operated using digital processes and technologies such as BIM, machine learning is also starting to appear in many of the tools that architects and engineers use daily. Companies such as Autodesk are investing heavily and their technology previews, such as Project Dreamcatcher and Project Fractal, hint toward a future where machines are able to ‘design’.

The insight we can derive from data, and the patterns it reveals in our actions, selections, behaviours and choices, combine to bestow great power on those companies that can own, control and interpret it. Data has become ‘the new oil’ that drives growth for organisations such as Facebook or Google that are competing for your personal information.

New power, new opportunities, new risks and new threats: they are all changing the way our industry operates. In other words, they are all combining to become the ‘new normal’.

1/2 dreamcatcher

2/2 dreamcatcher