Experimental investigations of the effect of furniture on the vibration performance of lightweight timber floors

This study presents an experimental investigation of the vibration performance of lightweight timber-joisted floors under different furniture arrangements. Three floors with identical geometry and varying construction were tested to evaluate the effect of furniture, as a live load distribution, on modal parameters and human-induced dynamic responses. The results showed that adding furniture to the bare floors improved vibration performance, leading to a reduction in dynamic responses. However, for furnished floors, different furniture arrangements significantly influenced the floor dynamics, highlighting the importance of considering realistic live load distributions in the vibration assessment of lightweight floors. In order to predict the dynamic responses, the simplified response prediction method in prEN 1995 was found to overestimate RMS accelerations and did not accurately capture the observed behavior. To better understand the relationship between fundamental modal parameters and dynamic responses, normalized parameters incorporating modal mass, mode shape, and damping were introduced, improving correlation with RMS accelerations. Finally, a predictive model for the furniture reduction factor indicated that, in addition to modal parameters, walking direction, path, and furniture arrangement were the most influential predictors, providing a practical tool for timber floor vibration design. • Experimental evaluation of lightweight timber floors showed a strong influence of furniture arrangements on floor dynamic behaviour. • Furniture increases the energy dissipation, and different furniture arrangements can alter the floor modal parameters significantly. • Normalized modal parameters ( ϕ 1 / M ∗ 1 and ϕ 1 /( M ∗ 1 ζ 1 )) improved correlation with measured RMS accelerations compared to using M ∗ 1 alone. • Simplified prediction method in prEN 1995 overestimates acceleration responses and does not match observed responses. • Predictive model for the furniture reduction factor identified walking direction, path, and furniture layout as key predictors.