Damping Assessment of Lightweight Timber Floors Under Human Walking Excitations

Vibrations on timber floors are among the most common serviceability problems in social housing projects. The presence of low damping levels on these floors could cause excessive vibrations in a range of frequency and amplitude that generate discomfort in users. This study focuses on the influence of the damping ratio in the dynamic serviceability of social housing timber floors due to walking excitations. More than 60 human-walking vibration tests were conducted on both laboratory and in-situ timber floors. The floors were instrumented with accelerometers, and fundamental modal damping ratios were estimated by applying Enhanced Frequency Decomposition Domain (EFDD) and Subspace Stochastic Identification (SSI) methods. The vibration dose value (VDV) was used to estimate the dynamic serviceability of floors. The results indicated that timber floors had an impulsive-type vibration response, with fundamental damping ratios between 1.9% and 14.8%, depending on their constructive characteristics. The in-situ floors had damping ratios between two to three times greater than the laboratory floors due to the presence of non-structural elements. Finally, it was possible to demonstrate that the floors with the highest damping ratios reached lower vibration dose values and, therefore, a better dynamic serviceability performance.

From Statistical Transportability to Estimating the Effect of Stochastic Interventions

Learning systems often face a critical challenge when applied to settings that differ from those under which they were initially trained. In particular, the assumption that both the source/training and the target/deployment domains follow the same causal mechanisms and observed distributions is commonly violated. This implies that the robustness and convergence guarantees usually expected from these methods are no longer attainable. In this paper, we study these violations through causal lens using the formalism of statistical transportability [Pearl and Bareinboim, 2011] (PB, for short). We start by proving sufficient and necessary graphical conditions under which a probability distribution observed in the source domain can be extrapolated to the target one, where strictly less data is available. We develop the first sound and complete procedure for statistical transportability, which formally closes the problem introduced by PB. Further, we tackle the general challenge of identification of stochastic interventions from observational data [Sec.~4.4, Pearl, 2000]. This problem has been solved in the context of atomic interventions using Pearl's do-calculus, which lacks complete treatment in the stochastic case. We prove completeness of stochastic identification by constructing a reduction of any instance of this problem to an instance of statistical transportability, closing the problem.