“Unlocking the Secrets of Wood Deterioration: How Data and Chemistry are Revolutionizing Building Maintenance”

The use of wood in construction has a long history, but in recent years, it has been making a comeback due to its environmental benefits. However, one of the main challenges with using wood in construction is its susceptibility to damage from sunlight and moisture, especially when used outdoors. Wood coatings have been developed to protect wooden surfaces, but often the damage starts before it becomes visible to the naked eye.

To address this issue, a team of researchers at Kyoto University is working on a novel method to diagnose the nearly invisible deterioration of wood coatings before it becomes irreparable. Led by corresponding author Yoshikuni Teramoto, the team is combining mid-infrared spectroscopy with machine learning to develop a data-driven tool for early detection of coating deterioration.

By testing artificially weathered wood coatings and coatings containing cellulose nanofiber, the researchers have been able to create a model using partial least square technique to predict the extent of deterioration. They have also used a genetic algorithm to identify the most informative infrared signals, improving the accuracy and interpretability of their model.

The team’s method allows them to detect subtle chemical changes and estimate the level of deterioration with high accuracy, enabling quick diagnosis without damaging the wood. This approach could reduce the need for costly visual inspections by detecting early warning signs of deterioration and preventing further decay.

In addition to wood, the researchers believe that their method could be applied to other materials like concrete or metal, opening up new possibilities for diagnosing early material failure and improving sustainability in various industries. They are currently conducting tests on real wooden buildings and plan to further develop their model for application in new paint and coating product development.

Overall, the research team’s innovative approach demonstrates how chemistry and data-driven modeling techniques can support smarter maintenance of sustainable buildings, bridging the gap between traditional craftsmanship and modern data science. Their work has the potential to revolutionize the way we approach building maintenance and sustainability in the construction industry.