Development of a new acoustic prediction tool by integration of life cycle assessment

Recently, environmental awareness has been the key driver for using renewable materials that have low environmental impact and fulfill constructional requirements, such as timber. Despite the advantages of wood as a building material, it has a lower subjective quality of sound insulation. To fulfill the sound insulation requirements, it is, therefore, unavoidable to complement based-wooden assemblies with additional element(s). However, identifying the acoustic performance is costly and time-consuming. Therefore, developing an accurate prediction tool is vital. Since wood-based structures have been developed to consider the environmental aspects, the environmental performance of buildings should be integrated into the acoustic design. This paper aims to develop an acoustic design methodology for wooden structures using artificial neural network approach by integration of life cycle assessment (LCA). Various Lab-based measurements are used to develop the acoustic prediction tool. Then, a LCA study is conducted on the test assemblies. This paper initially found that wooded assemblies generally increase the environmental impacts to achieve better acoustic insulation. Moreover, different assemblies can meet the sound insulation requirements. Therefore, designers should cognize of environmental and acoustic trade-off by selecting assemblies that consider both aspects.