It’s what’s inside that counts: an assessment method to measure the residual strength of anobiids infested timber using micro-computed tomography

The safety assessment of old timber structures is an important issue due to the long-term behavior of wood and the structural complexity found in some older constructions. If the structure is de-graded due to the action of wood-boring insects, the complexity of the analysis increases.
The objective of the work reported in this paper is to provide an assessment method to measure the residual strength of pine structural elements degraded by anobiids. Samples of degraded tim-ber were submitted to micro-computed tomography (μ-XCT) to quantify density loss being this parameter of fundamental importance for the assessment of timber structures as it is highly corre-lated with timber mechanical properties. During the μ-XCT study an empirical correlation be-tween lost material percentage (consumed by beetles) and timber apparent densities (original – before degradation and residual – after degradation) was established.
The results showed an experimental high correlation (r2=0.66) between original apparent density and lost material percentage and an even higher correlation (r2=0.87) between residual apparent density and lost material percentage which confirms that μ-XCT can be validly used contributing to the 3D visualization and quantification of timber degraded elements.
After the μ-XCT study, screw withdrawal and shear parallel to grain tests have been made in mar-itime pine degraded timber. Screw withdrawal force and shear strength values were related with density loss (r2=0.64 for screw withdrawal; r2=0.65 for shear strength parallel to grain). A novel assessment method for evaluating the impact of anobiid damage on timber degraded structural elements based in four major steps is proposed enabling a more quantitative assessment of the timber elements residual strength and, therefore, contributing to reduce unnecessary replacement and to provide foundations required to perform experimental modelling tests.