A comparison of numerical approaches to quantify sound insulation of lightweight wooden floor structures

• Quantifying air-borne and structure-borne sound insulation is an important design consideration for the indoor comfort in a building. Although sound insulation performance is commonly measured experimentally, numerical methods can have time-saving and economic benefits. Further, numerical methods can be incorporated within building simulations to provide an estimate of the acoustic environment. In response, this paper evaluates three different computational approaches for quantifying sound insulation in one-third octave bands (50 Hz -5 kHz) of a lightweight floor including: an analytical (theoretical) model, a finite element model (FEM), and an artificial neural network (ANN) model. The three numerical methods are tested on the sound insulation of a cross laminated timber (CLT) floor. The results of this study show that the ANN model is able to accurately predict the air-borne and impact sound insulation performance at frequencies above 250 Hz, but over-predicts the air-borne performance and under-predicts the impact performance at low frequencies. However, the analytical and FEM strategies provide acceptable estimations, useful during the conceptual design stage, but with higher deviations than ANN model across all frequencies. While no model is able to accurately represent acoustic behavior across all frequencies, this work highlights the advantages and disadvantages when applied to predicting the sound insulation of a CLT floor.

Subject headings

TEKNIK OCH TEKNOLOGIER  -- Maskinteknik -- Strömningsmekanik och akustik (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Mechanical Engineering -- Fluid Mechanics and Acoustics (hsv//eng)

Keyword

artificial neural networks

building acoustics

floor structures

numerical analysis

sound insulation

Publication and Content Type

kon (subject category)

ref (subject category)