CT scanning unlocks hidden value in reclaimed timber for construction
Researchers have developed a method to precisely measure the strength of reclaimed wood beams using CT scanning and computational modeling, eliminating guesswork in reuse decisions. The breakthrough could accelerate the circular economy in construction by making it economically viable to salvage and repurpose structural timber from demolished buildings.
Originaltitel: Comparative Evaluation of Non-destructive and CT-derived Indicators for Stiffness Assessment of Reclaimed Norway Spruce Timber
This preprint presents a multimodal evaluation framework for reclaimed structural timber, combining X-ray computed tomography (CT), visual grading, dynamic excitation, four-point bending tests, and two classes of computational models (continuum-mechanics models and finite element analyses). Using 56 full-length reclaimed beams, the study compares the spatial sensitivity and predictive performance of established non-destructive techniques and CT-derived stiffness and density fields. The results demonstrate how voxel-resolved CT information can quantify stiffness- and defect-related variability and how these indicators relate to global and local mechanical performance. The analyses include:• Full-resolution CT-derived density and fibre-orientation fields• Longitudinal and transversal dynamic excitation measurements• Destructive and non-destructive mechanical bending tests• Visual grading according to INSTA 142, UNI 11119, and NS 3691-3• CT-based continuum-mechanics stiffness models• CT-informed finite element models with orthotropic material fields• Correlation, regression, and indicator-comparison analyses at beam and zone scales All underlying CT volumes, derived stiffness fields, CM and FE model outputs, profile-level results, and statistical analysis plots are openly available in the associated dataset: Dataset:Huber, J.A.J. et al. (2025). Multimodal Reclaimed Timber Dataset: X-ray CT Volumes, Visual Grading, Dynamic Tests, Mechanical Tests, and Finite Element and Continuum Model Results. Zenodo. DOI: https://doi.org/10.5281/zenodo.17682666 This preprint should be cited together with the dataset when reusing data, analysis workflows, or modelling approaches.