Wood and Building Technologies

Within the research area Wood and Building Technologies, the objective of Linnaeus University Centre for Data Intensive Sciences and Applications (DISA) is to create more intelligent decision support systems for the wood industry. The work includes identifying relationships among different properties of wood-based materials by using Big Data, for example.

Our research

The researchers within Wood and Building Technologies work with fundamental and applied questions, with the aim to create more intelligent decision support systems for the wood industry. Part of the strategy is to identify relationships among different measurable properties of wood-based materials.

Work with computational intensive methods, advanced statistics and very large sets of data will be included. This requires competence in the fields of computer science and statistics, in addition to knowledge of wood material. Some examples:

  • Strength, stiffness and shape stability are decisive for the competitiveness of wood – the only renewable structural material for construction. Timber can be graded with respect to these important engineering properties, but higher utilization of the material requires more accurate grading than what can be obtained using the methods on the market today. This will be possible by using the latest advances of information technology.
  • Applications of Big Data in the forest-based industry and furniture domain such as intelligent personalized generation of wood and furniture product bundles (methods and software), support of smart business network operations, development of expert systems based on intelligent models of wood and furniture sector manufacturing, data analysis for decision making, and development of intelligent wood and furniture operations through the integration of advanced control systems (based on micro-controllers, for example) and sensors/actuators.

Wood and Building Technologies is an application area within the Linnæus University Centre of Excellence (LNUC) for Data Intensive Sciences and Applications.

Foto: Peter Ekberg

Staff