Predicting and optimising the airborne sound transmission of floor–ceiling constructions using computational intelligence

Computational intelligence (CI) techniques offer powerful alternatives for investigating acoustical issues and providing acoustical solutions to problems. This paper presents information on two CI techniques by applying them to the sound transmission performance prediction and design of floor–ceiling constructions. First a simple neural network (NN) model for predicting the airborne sound transmission of typical floor–ceiling constructions is presented and explained in detail. This model is accessible to researchers with knowledge of neural network analysis (NNA) for further sophistication, specialisation or hybridisation. The model may also be used by architects and others with no knowledge of NNA and no access to any specialised neural network software. Evolutionary algorithms (EAs) were then applied to search the multidimensional space created by the neural network model in order to optimise the airborne sound transmission of floor–ceiling constructions within the range of design parameters utilised in buildings.