Wood is a priceless constructing material. It’s beneficial to wellbeing and energy-efficient. But the thing that makes it priceless - it being natural - also presents a weakness: it has a life-cycle so it’s subjected to changing and aging. Here’s technology to the rescue!
With machine learning, we can see how the wood looks from the inside, not just outside.
The WOOLF project
The WOOLF project develops wooden structural and window systems with newly developed sensor technology integrated into them, which enables real-time monitoring the quality of wood and predicting their lifespan.
The WOOLF project aims to solve the key problems of wooden construction via preventive and transparent measures.
XLAB’s role in the project
Artificial intelligence, data analytics, and machine learning help us understand the wood-aging process better. We basically use machine learning to ‘look into’ the wood. How?
We analyze data from aged wood with machine learning models and identify key changes, happening during the aging process. With machine learning algorithms we can determine how quickly the changes in weather conditions reflect in the wood.
This graph shows how the algorithm learns the characteristics of wood moisture and then uses the gained knowledge to detect anomalies.
Gained data helps us predict the moisture and temperature in the wood and because the moisture and temperature determine the speed of wood aging processes, that helps us predict its life span - and prolong it by sensing and solving problems before they even occur.
Together with our project partners, we prove technology isn’t something separate from nature - it can help us understand it.
