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Glulam beams exposed to outdoor conditions

VILBAR Visual Inspection of glulam for Load-Bearing Applications Reuse

The project develops an objective, field-applicable method for inspecting glulam and enabling safe reuse. Using optical sensing, image analysis, and AI, internal defects can be predicted from surface measurements—creating quality-assured and scalable solutions for the construction sector.

The project VILBAR – Visual Inspection of glulam for Load-Bearing Applications in Reuse – aims to enable and streamline the reuse of glulam components in the construction sector, with a focus on structural applications. 

The background is that the building and real estate sector accounts for a significant share of Sweden’s climate impact, and that the EU taxonomy and new regulations, such as the Ecodesign for Sustainable Products Regulation (ESPR), are increasing the requirements for reduced emissions and circular solutions. Reuse of wood, particularly glulam, has the potential to reduce resource consumption, climate impact and waste, while strengthening carbon storage and economic value in Sweden.

Demand for timber is increasing as more sectors seek to reduce their climate footprint, while supply may be affected by a growing need to allocate forest areas for conservation and other societal objectives. To reduce the extraction of natural resources and the amount of waste, it is necessary to increase the reuse of building components, thereby extending material lifetimes and contributing to long-term carbon storage. Despite this, the reuse of glulam remains limited, mainly due to the lack of reliable and practically applicable methods for assessing the quality of used components in the field.

The aim of the project is to develop an objective, repeatable and field-applicable methodology for inspecting glulam, ensuring that reused components meet performance requirements. This includes developing prototype equipment and processes to assess both surface and subsurface properties of glulam beams using optical sensing, image processing and machine learning. The goal is to enable prediction of internal defects such as cracks and fiber disturbances based on surface measurements, leading to safe, quality-assured and economically viable reuse processes.

Summary

Project name

VILBAR

Status

Active

Region

Region Västerbotten

RISE role in project

Projektledare, utför tester och beräkningar

Project start

Duration

12 månader

Total budget

1 300 000 kr

Funders

Stiftelsen Nils och Dorthi Troëdssons forskningsfond

Coordinators

Project members

Supports the UN sustainability goals

9. Industry, innovation and infrastructure
Karin Sandberg

Contact person

Karin Sandberg

Senior Forskare

+46 10 516 62 41

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Benedikt Neyses

Contact person

Benedikt Neyses

Forskare

+46 10 228 40 64

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Contact Benedikt
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