Your search resulted in 404 documents. Displaying 25 entries per page.
AI Models for Accelerating Wood Protection Testing: Development of Predictive Tools Based on Long-Term Field Test Data
2025 - IRG/WP 25-41023
In this study, we present the development of DecAI, an artificial intelligence (AI) model designed to optimise and accelerate performance evaluations of wood protection products in the European EN 330:2014 "Field test: L-joint method." The Danish Technological Institute (DTI) compiled a dataset of over 100,000 data points from approx.10,000 L-joint samples collected over +15 years at field sites i...
J Stenbaek, B Noufel, L Glade, P Bisgaard
Quantitative Prediction of Latent Deterioration in Wood Coatings Using Mid-Infrared Spectroscopy and Machine Learning
2025 - IRG/WP 25-41038
Wood coatings play a vital role in prolonging the lifespan of timber structures by protecting them from environmental degradation. However, conventional evaluation methods rely on visual inspections, which cannot detect latent deterioration before visible damage occurs. This study integrates attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy with partial least squares ...
Y Teramoto
Investigation of Impregnation Factors for Biomass-Based Phenol-Formaldehyde Resins
2025 - IRG/WP 25-41044
This study investigates the impregnation factors affecting the treatment of wood with biomass-based phenol-formaldehyde (PF) resins, such as impregnation methods, resin properties, and setting parameters like time and pressure. The goal is to ensure effective resin penetration into the wood cell wall structure, thereby enhancing wood durability and mechanical properties. Previous literature has la...
Y-C Huang, T-H Lin, P-Y Kuo
Development of an Early Degradation Diagnosis Technology for Wood Coatings Using Mid-Infrared Spectroscopy and Machine Learning
2025 - IRG/WP 25-41047
Accurately predicting the degradation state of wood coatings is challenging, and as the coating deteriorates, it becomes a major factor accelerating the degradation of the wood itself. In this study, we aimed to develop a diagnostic technology that combines mid-infrared spectroscopy and machine learning to assess latent coating degradation, which cannot be detected visually, and to enable appropri...
K Nishimura, T Ito, S Isaji, T Takano, H Ohki, Y Teramoto