Use Case

Sinking but managing your risks

Machine Learning vs Subsidence (English)

Subsidence is a living nightmare for property owners, where the ground under your building collapses, often causing extensive structural damage and costing thousands to repair. However, a new AI-enabled predictive model could reduce the devastating impact of subsidence.

Data scientists from Intellerts recently joined forces with the Parteon housing corporation to predict the likelihood of subsidence in a region of the Netherlands where this issue is a prevalent problem.

After analysing more than 136 million records, our foundation repair model could identify buildings with a high risk of subsidence, allowing the region to make crucial savings on both manual investigation and any ensuing repair costs.

Zaanstad’s sinking feeling

Subsidence is a serious problem in Zaanstad, a municipality with 152,000 residents and where approximately 70,000 buildings are built on wooden piles on soft ground. The wood is susceptible to bacterial damage and this reduces the foundations’ load-bearing capacity.

Consequently, one-third of the municipality’s total housing stock is predicted to have foundation problems – but the true extent of this risk is unknown for the vast majority of properties.

This is because it is both expensive and time-consuming to manually investigate a property’s foundations and fix any damage, with an average repair costing between 30,000 and 50,000 Euros per house.

So, Intellerts was tasked with answering this question for Zaanstad: can a predictive model accurately assess the quality of the foundations of a building to reduce these costs and timescales?

Big Data for a big problem

From a data analytics perspective, this was a complex challenge. Not only do bad foundations not necessarily lead to catastrophic subsidence immediately, but there were also many other factors to consider.

These included the soil type (mostly clay or peat), water level, presence of trees that may remove moisture from the ground underneath, the drainage system, and vibrations from any nearby building work.

Intellerts’ data scientists had plenty of information to work with, including 140 gigabytes of data, which is equivalent to around 136 million records.

The data did not only come from the municipality and Parteon, but also from external parties such as the KNMI, the country’s meteorological society, and the Kadaster government agency.

“Linking all these files was a time-consuming task,” explains Martin Haagoort, managing director and data scientist at lntellerts. “There were a considerable number of transforma­tions required to get all the data at the same level so that it could be analysed as a whole.”

Satellite data was also used during the analysis, which contained detailed records for the period between 1992 and 2016. By mathematically analysing the radar images from the satellites, the subsidence per building could be determined to the nearest millimetre.

Model results

Using all of this information, a predictive model was developed by Intellerts using a range of cutting-edge machine learning algorithms.

By using algorithms that continually learn from data, machine learning ensures that new trends, patterns and insights can be uncovered that may not have been identified by manual analysis methods alone.

The resulting foundation repair model was then used to categorise the foundations every building in Zaanstad, even though measurements or expert estimates were not available for most properties. “This has unprecedented advantages in terms of planning and costs in the management of foundation repair,” according to Levinus Jongmans, who is responsible for foundation repair at the  Municipality of Zaanstad.

The results from this model were also in agreement with Parteon’s existing building information and foundation reports. “The outcome was very encouraging,” says Jurgen de Ruiter, Parteon’s CFO. “Not only is the model of high value, but it gives extensive information that allows separation into five risk categories, from low (monitoring is enough) to very high (action required).”

“Now, we have on record that 11,000 homes are at risk. That is between 15 and 20 per cent of the total housing stock in Zaanstad. We have a better idea of which buildings we have to work on, and when. This means we can make crucial savings on investigation costs.” Intellerts helps a wide range of businesses unlock the power of AI and data science to stay ahead of the competition. If you’d like to find out more about how we could help your organisation, please click here to find out more and contact one of our team today.


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