Formula 1 motorsport has changed dramatically over the last decade. Spectators watch the cars racing around the track, but what they don’t see are the constant streams of data being transmitted in real time from the car to the pits.
Each F1 car has between 200 and 300 sensors, transmitting around 30MB of data per lap. Much of that data is streamed live to engineering teams often located at the team’s HQ in another country with a latency as low as 60 milliseconds.
That same kind of live telemetry is now starting to filter into buildings.
Real-Time Building Data
Today, it’s already possible to log in remotely and monitor how a building is performing both live and historically. This includes temperature, humidity, water use, and even how the occupants are interacting with the space (heating, ventialtion and electric useage).
Sensors in buildings are nothing new. What is new is how we analyse the data they produce.
This shift in data analysis opens up a whole new range of diagnostics for building surveyors. Rather than relying solely on periodic inspections, surveyors can now analyse environmental data across multiple buildings. Using AI, they can detect patterns or identify anomalies far earlier than traditional methods allow.
Diagnosing Dampness: A Common Challenge
Dampness is one of the most frequently encountered issues in buildings, whether from leaking plumbing, defective rainwater goods, or failing external fabric.
In some cases, the source is clear. In others, it remains a mystery until tell-tale signs appear: fruiting bodies, black mould, or salt deposits on internal surfaces.
I recall a story from my university days, told by a respected professor of building surveying. He investigated a flat where the tenant consistently reported a damp living room floor. Despite repeated inspections, no source could be found.
One day, he arrived unannounced and discovered the carpet was saturated. It turned out the occupant had been deliberately watering the carpet with a watering can, just before he arrived.
AI vs Human Guesswork
Historically, surveyors relied on experience, observation, and periodic monitoring. But AI now makes it easier to build an accurate picture of what’s happening inside a property, without multiple site visits.
Take mould growth, for example. It’s often caused by the behaviour of occupants: drying wet clothes indoors, blocking vents, or failing to ventilate after hot showers. But proving these behaviours can be difficult.
By installing sensors to monitor the like of, humidity, temperature, CO₂, and water flow (on the cold main and boiler) and using 4G or 5G to transmit data in real time, a building can now be monitored continuously. AI can then analyse this data alongside external weather patterns and known conditions for mould growth.
Example Scenario
A tenant reports mould growth in a flat. A visual inspection confirms the presence of mould, but no source of moisture is immediately visible.
Sensors are installed in January:
- Cold water usage spikes between 9pm and 10pm on Wednesdays, Fridays, and Sundays
- Humidity in the living room rises 10% above the baseline 2–3 hours later after the spike in water flow
- CO₂ levels fall in the living room but rise in the bedrooms from 11pm
- Temperatures in all rooms drop after 11.30pm and rise again after 5.30am
- Boiler activity stops at 11pm and resumes at 5.15am
- External night temperatures average 1°C, with lows down to -8°C
AI analyses the data from the sensors along with weather records for the month. It determines at which points environmental conditions that are necessary for mould growth, were present and at what times and days in January they ocuured. (e.g. most likely washing clothes in the evening and drying them indoors overnight).
The Future of Building Diagnostics
In the coming years, more new homes will have embedded smart sensors, feeding real-time data to cloud-based platforms.
AI systems will continuously monitor for emerging problems. Data from similar property types can be compared to identify anomalies or early warning signs.
As soon as the risk of a problem is detected, the homeowner or landlord can be alerted to take preventive action. Or, automated systems can intervene: for example, activating ventilation remotely in response to rising humidity.
Building surveyors will be able to access this data in advance of a site visit, offering insights into how the building performs and how it’s used, just like an F1 engineer monitoring every component of a car mid-race.