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Design for disassembly using deconstructable connections facilitates recycling and reusing of building materials and, therefore, reduces waste management problems at the end of service life. In this regard, a deconstructable timber-concrete composite connector using self-tapping screws has been developed at Aalto University. In the presented research, an experimental investigation was performed to further evaluate the effectiveness of this connector in fabricating deconstructable cross-laminated timber (CLT)-concrete composite floors. For this purpose, several CLT-concrete composite beams were fabricated using 5-layer CLT and low-shrinkage concrete. Each beam contained one row of connectors to represent a strip of a full-scale floor. The vibration performance, bending properties, interface slip, failure modes, and ease of disassembly of the beams were investigated. The results were compared with the ones from a reference group of beams fabricated with regular screws. Overall, the deconstructable beams performed exceptionally well by attaining 98.5% of the average bending stiffness of the regular beams. The load-carrying capacity was also similar but governed by the CLT plate. The vibration characteristics were comparable in both groups. After the bending test, the deconstructable beams were disassembled. Although the beams had been exposed to unproportionally large deformations under the bending load, the disassembly process was performed successfully.

The main goal of this study was to investigate the visual characteristics, recovery rate, and flexural properties of sawn boards from a fibre-managed plantation Eucalyptus globulus resource as a potential raw material for structural building applications. The impacts of the visual characteristics, strength-reducing features, and variation in basic density and moisture content on the bending modulus of elasticity (MOE) and modulus of rupture (MOR) of the boards were investigated. The reliabilities of different non-destructive methods in predicting MOE and MOR of the boards were evaluated, including log acoustic wave velocity measurement and numerical modellings. The MOE and MOR of the boards were significantly affected by the slope of grain, percentage of clear wood, and total number of knots in the loading zone of the boards. The normal variation in basic density significantly influenced the MOE of the boards while its effect on the MOR was insignificant. The numerical models developed using the artificial neural network (ANN) showed better accuracies in predicting the MOE and MOR of the boards than traditional multi-regression modelling and log acoustic wave velocity measurement. The ANN models developed in this study showed more than 78.5% and 79.9% success in predicting the adjusted MOE and MOR of the boards, respectively.